Overview

Dataset statistics

Number of variables46
Number of observations84
Missing cells479
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory95.3 KiB
Average record size in memory1.1 KiB

Variable types

Numeric28
Categorical11
Text3
DateTime1
Boolean1
Unsupported2

Alerts

DataYear has constant value ""Constant
City has constant value ""Constant
State has constant value ""Constant
DefaultData has constant value ""Constant
ComplianceStatus has constant value ""Constant
OSEBuildingID is highly overall correlated with TaxParcelIdentificationNumberHigh correlation
TaxParcelIdentificationNumber is highly overall correlated with OSEBuildingID and 2 other fieldsHigh correlation
CouncilDistrictCode is highly overall correlated with Longitude and 1 other fieldsHigh correlation
Latitude is highly overall correlated with BuildingType and 3 other fieldsHigh correlation
Longitude is highly overall correlated with CouncilDistrictCode and 1 other fieldsHigh correlation
YearBuilt is highly overall correlated with PropertyGFAParkingHigh correlation
PropertyGFATotal is highly overall correlated with PropertyGFABuilding(s) and 8 other fieldsHigh correlation
PropertyGFAParking is highly overall correlated with YearBuiltHigh correlation
PropertyGFABuilding(s) is highly overall correlated with PropertyGFATotal and 8 other fieldsHigh correlation
LargestPropertyUseTypeGFA is highly overall correlated with PropertyGFATotal and 7 other fieldsHigh correlation
SecondLargestPropertyUseTypeGFA is highly overall correlated with PropertyGFATotal and 7 other fieldsHigh correlation
ThirdLargestPropertyUseTypeGFA is highly overall correlated with BuildingType and 4 other fieldsHigh correlation
ENERGYSTARScore is highly overall correlated with SiteEUI(kBtu/sf) and 6 other fieldsHigh correlation
SiteEUI(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 7 other fieldsHigh correlation
SiteEUIWN(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 8 other fieldsHigh correlation
SourceEUI(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 8 other fieldsHigh correlation
SourceEUIWN(kBtu/sf) is highly overall correlated with ENERGYSTARScore and 8 other fieldsHigh correlation
SiteEnergyUse(kBtu) is highly overall correlated with PropertyGFATotal and 13 other fieldsHigh correlation
SiteEnergyUseWN(kBtu) is highly overall correlated with PropertyGFATotal and 13 other fieldsHigh correlation
Electricity(kWh) is highly overall correlated with PropertyGFATotal and 10 other fieldsHigh correlation
Electricity(kBtu) is highly overall correlated with PropertyGFATotal and 10 other fieldsHigh correlation
NaturalGas(therms) is highly overall correlated with NaturalGas(kBtu) and 6 other fieldsHigh correlation
NaturalGas(kBtu) is highly overall correlated with NaturalGas(therms) and 6 other fieldsHigh correlation
TotalGHGEmissions is highly overall correlated with PropertyGFATotal and 13 other fieldsHigh correlation
GHGEmissionsIntensity is highly overall correlated with ENERGYSTARScore and 8 other fieldsHigh correlation
BuildingType is highly overall correlated with Latitude and 16 other fieldsHigh correlation
PrimaryPropertyType is highly overall correlated with ThirdLargestPropertyUseTypeGFA and 4 other fieldsHigh correlation
Neighborhood is highly overall correlated with CouncilDistrictCode and 5 other fieldsHigh correlation
NumberofBuildings is highly overall correlated with Latitude and 4 other fieldsHigh correlation
LargestPropertyUseType is highly overall correlated with PrimaryPropertyType and 1 other fieldsHigh correlation
SecondLargestPropertyUseType is highly overall correlated with TaxParcelIdentificationNumber and 4 other fieldsHigh correlation
ThirdLargestPropertyUseType is highly overall correlated with TaxParcelIdentificationNumber and 9 other fieldsHigh correlation
BuildingType is highly imbalanced (61.0%)Imbalance
NumberofBuildings is highly imbalanced (81.1%)Imbalance
SecondLargestPropertyUseType has 39 (46.4%) missing valuesMissing
SecondLargestPropertyUseTypeGFA has 39 (46.4%) missing valuesMissing
ThirdLargestPropertyUseType has 62 (73.8%) missing valuesMissing
ThirdLargestPropertyUseTypeGFA has 62 (73.8%) missing valuesMissing
YearsENERGYSTARCertified has 82 (97.6%) missing valuesMissing
ENERGYSTARScore has 27 (32.1%) missing valuesMissing
Comments has 84 (100.0%) missing valuesMissing
Outlier has 84 (100.0%) missing valuesMissing
OSEBuildingID has unique valuesUnique
PropertyName has unique valuesUnique
Address has unique valuesUnique
PropertyGFATotal has unique valuesUnique
PropertyGFABuilding(s) has unique valuesUnique
LargestPropertyUseTypeGFA has unique valuesUnique
SourceEUIWN(kBtu/sf) has unique valuesUnique
SiteEnergyUse(kBtu) has unique valuesUnique
SiteEnergyUseWN(kBtu) has unique valuesUnique
Electricity(kWh) has unique valuesUnique
Electricity(kBtu) has unique valuesUnique
TotalGHGEmissions has unique valuesUnique
Comments is an unsupported type, check if it needs cleaning or further analysisUnsupported
Outlier is an unsupported type, check if it needs cleaning or further analysisUnsupported
PropertyGFAParking has 54 (64.3%) zerosZeros
SecondLargestPropertyUseTypeGFA has 4 (4.8%) zerosZeros
ThirdLargestPropertyUseTypeGFA has 3 (3.6%) zerosZeros
SteamUse(kBtu) has 63 (75.0%) zerosZeros
NaturalGas(therms) has 14 (16.7%) zerosZeros
NaturalGas(kBtu) has 14 (16.7%) zerosZeros

Reproduction

Analysis started2023-08-01 10:35:56.130514
Analysis finished2023-08-01 10:37:50.539106
Duration1 minute and 54.41 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

OSEBuildingID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.119048
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:51.438283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.15
Q128.5
median62.5
Q3104.5
95-th percentile138.55
Maximum163
Range162
Interquartile range (IQR)76

Descriptive statistics

Standard deviation43.132021
Coefficient of variation (CV)0.64261968
Kurtosis-1.0683919
Mean67.119048
Median Absolute Deviation (MAD)38
Skewness0.25130736
Sum5638
Variance1860.3712
MonotonicityStrictly increasing
2023-08-01T12:37:51.599864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
85 1
 
1.2%
103 1
 
1.2%
102 1
 
1.2%
100 1
 
1.2%
98 1
 
1.2%
96 1
 
1.2%
95 1
 
1.2%
89 1
 
1.2%
86 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
5 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
11 1
1.2%
12 1
1.2%
15 1
1.2%
ValueCountFrequency (%)
163 1
1.2%
147 1
1.2%
145 1
1.2%
144 1
1.2%
139 1
1.2%
136 1
1.2%
132 1
1.2%
131 1
1.2%
121 1
1.2%
120 1
1.2%

DataYear
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2016
84 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters336
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 84
100.0%

Length

2023-08-01T12:37:51.741245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:51.872384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2016 84
100.0%

Most occurring characters

ValueCountFrequency (%)
2 84
25.0%
0 84
25.0%
1 84
25.0%
6 84
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 84
25.0%
0 84
25.0%
1 84
25.0%
6 84
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 84
25.0%
0 84
25.0%
1 84
25.0%
6 84
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 84
25.0%
0 84
25.0%
1 84
25.0%
6 84
25.0%

BuildingType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
NonResidential
73 
Nonresidential COS
10 
Campus
 
1

Length

Max length18
Median length14
Mean length14.380952
Min length6

Characters and Unicode

Total characters1208
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowNonResidential
2nd rowNonResidential
3rd rowNonResidential
4th rowNonResidential
5th rowNonResidential

Common Values

ValueCountFrequency (%)
NonResidential 73
86.9%
Nonresidential COS 10
 
11.9%
Campus 1
 
1.2%

Length

2023-08-01T12:37:51.983500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:52.124875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nonresidential 83
88.3%
cos 10
 
10.6%
campus 1
 
1.1%

Most occurring characters

ValueCountFrequency (%)
n 166
13.7%
e 166
13.7%
i 166
13.7%
a 84
7.0%
s 84
7.0%
t 83
6.9%
l 83
6.9%
o 83
6.9%
N 83
6.9%
d 83
6.9%
Other values (9) 127
10.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1011
83.7%
Uppercase Letter 187
 
15.5%
Space Separator 10
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 166
16.4%
e 166
16.4%
i 166
16.4%
a 84
8.3%
s 84
8.3%
t 83
8.2%
l 83
8.2%
o 83
8.2%
d 83
8.2%
r 10
 
1.0%
Other values (3) 3
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 83
44.4%
R 73
39.0%
C 11
 
5.9%
O 10
 
5.3%
S 10
 
5.3%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1198
99.2%
Common 10
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 166
13.9%
e 166
13.9%
i 166
13.9%
a 84
7.0%
s 84
7.0%
t 83
6.9%
l 83
6.9%
o 83
6.9%
N 83
6.9%
d 83
6.9%
Other values (8) 117
9.8%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 166
13.7%
e 166
13.7%
i 166
13.7%
a 84
7.0%
s 84
7.0%
t 83
6.9%
l 83
6.9%
o 83
6.9%
N 83
6.9%
d 83
6.9%
Other values (9) 127
10.5%

PrimaryPropertyType
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Hotel
37 
Other
18 
Small- and Mid-Sized Office
Mixed Use Property
K-12 School
Other values (8)
13 

Length

Max length27
Median length5
Mean length9.4285714
Min length5

Characters and Unicode

Total characters792
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)6.0%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Hotel 37
44.0%
Other 18
21.4%
Small- and Mid-Sized Office 8
 
9.5%
Mixed Use Property 4
 
4.8%
K-12 School 4
 
4.8%
Large Office 4
 
4.8%
Self-Storage Facility 2
 
2.4%
Senior Care Community 2
 
2.4%
University 1
 
1.2%
Warehouse 1
 
1.2%
Other values (3) 3
 
3.6%

Length

2023-08-01T12:37:52.245955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hotel 37
28.0%
other 18
13.6%
office 13
 
9.8%
small 8
 
6.1%
and 8
 
6.1%
mid-sized 8
 
6.1%
k-12 4
 
3.0%
school 4
 
3.0%
large 4
 
3.0%
property 4
 
3.0%
Other values (13) 24
18.2%

Most occurring characters

ValueCountFrequency (%)
e 106
13.4%
t 69
 
8.7%
l 64
 
8.1%
o 58
 
7.3%
48
 
6.1%
i 46
 
5.8%
r 39
 
4.9%
H 38
 
4.8%
O 31
 
3.9%
a 30
 
3.8%
Other values (27) 263
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 580
73.2%
Uppercase Letter 134
 
16.9%
Space Separator 48
 
6.1%
Dash Punctuation 22
 
2.8%
Decimal Number 8
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 106
18.3%
t 69
11.9%
l 64
11.0%
o 58
10.0%
i 46
7.9%
r 39
 
6.7%
a 30
 
5.2%
d 29
 
5.0%
f 28
 
4.8%
h 23
 
4.0%
Other values (11) 88
15.2%
Uppercase Letter
ValueCountFrequency (%)
H 38
28.4%
O 31
23.1%
S 27
20.1%
M 13
 
9.7%
U 5
 
3.7%
P 4
 
3.0%
K 4
 
3.0%
L 4
 
3.0%
C 4
 
3.0%
F 2
 
1.5%
Other values (2) 2
 
1.5%
Decimal Number
ValueCountFrequency (%)
1 4
50.0%
2 4
50.0%
Space Separator
ValueCountFrequency (%)
48
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 714
90.2%
Common 78
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 106
14.8%
t 69
 
9.7%
l 64
 
9.0%
o 58
 
8.1%
i 46
 
6.4%
r 39
 
5.5%
H 38
 
5.3%
O 31
 
4.3%
a 30
 
4.2%
d 29
 
4.1%
Other values (23) 204
28.6%
Common
ValueCountFrequency (%)
48
61.5%
- 22
28.2%
1 4
 
5.1%
2 4
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 106
13.4%
t 69
 
8.7%
l 64
 
8.1%
o 58
 
7.3%
48
 
6.1%
i 46
 
5.8%
r 39
 
4.9%
H 38
 
4.8%
O 31
 
3.9%
a 30
 
3.8%
Other values (27) 263
33.2%

PropertyName
Text

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.9 KiB
2023-08-01T12:37:52.477485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length44
Median length31
Mean length18.892857
Min length5

Characters and Unicode

Total characters1587
Distinct characters68
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)100.0%

Sample

1st rowMayflower park hotel
2nd rowParamount Hotel
3rd row5673-The Westin Seattle
4th rowHOTEL MAX
5th rowWARWICK SEATTLE HOTEL (ID8)
ValueCountFrequency (%)
seattle 20
 
8.2%
hotel 16
 
6.6%
8
 
3.3%
inn 6
 
2.5%
the 4
 
1.6%
suites 4
 
1.6%
hall 3
 
1.2%
and 3
 
1.2%
center 3
 
1.2%
building 3
 
1.2%
Other values (152) 174
71.3%
2023-08-01T12:37:52.901613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160
 
10.1%
e 160
 
10.1%
t 123
 
7.8%
a 95
 
6.0%
l 89
 
5.6%
o 86
 
5.4%
i 79
 
5.0%
n 72
 
4.5%
r 65
 
4.1%
S 55
 
3.5%
Other values (58) 603
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1013
63.8%
Uppercase Letter 331
 
20.9%
Space Separator 160
 
10.1%
Decimal Number 53
 
3.3%
Dash Punctuation 14
 
0.9%
Other Punctuation 10
 
0.6%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 160
15.8%
t 123
12.1%
a 95
9.4%
l 89
8.8%
o 86
8.5%
i 79
7.8%
n 72
 
7.1%
r 65
 
6.4%
h 30
 
3.0%
s 28
 
2.8%
Other values (15) 186
18.4%
Uppercase Letter
ValueCountFrequency (%)
S 55
16.6%
H 31
 
9.4%
C 31
 
9.4%
A 21
 
6.3%
E 19
 
5.7%
I 19
 
5.7%
T 18
 
5.4%
N 16
 
4.8%
P 14
 
4.2%
M 14
 
4.2%
Other values (15) 93
28.1%
Decimal Number
ValueCountFrequency (%)
1 12
22.6%
5 6
11.3%
0 6
11.3%
2 6
11.3%
3 6
11.3%
6 5
9.4%
8 4
 
7.5%
7 4
 
7.5%
9 2
 
3.8%
4 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 3
30.0%
& 3
30.0%
# 2
20.0%
/ 2
20.0%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1344
84.7%
Common 243
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 160
 
11.9%
t 123
 
9.2%
a 95
 
7.1%
l 89
 
6.6%
o 86
 
6.4%
i 79
 
5.9%
n 72
 
5.4%
r 65
 
4.8%
S 55
 
4.1%
H 31
 
2.3%
Other values (40) 489
36.4%
Common
ValueCountFrequency (%)
160
65.8%
- 14
 
5.8%
1 12
 
4.9%
5 6
 
2.5%
0 6
 
2.5%
2 6
 
2.5%
3 6
 
2.5%
6 5
 
2.1%
8 4
 
1.6%
7 4
 
1.6%
Other values (8) 20
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160
 
10.1%
e 160
 
10.1%
t 123
 
7.8%
a 95
 
6.0%
l 89
 
5.6%
o 86
 
5.4%
i 79
 
5.0%
n 72
 
4.5%
r 65
 
4.1%
S 55
 
3.5%
Other values (58) 603
38.0%

Address
Text

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.6 KiB
2023-08-01T12:37:53.162085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length29
Median length22
Mean length15.892857
Min length10

Characters and Unicode

Total characters1335
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)100.0%

Sample

1st row405 Olive way
2nd row724 Pine street
3rd row1900 5th Avenue
4th row620 STEWART ST
5th row401 LENORA ST
ValueCountFrequency (%)
ave 41
 
13.9%
st 20
 
6.8%
n 14
 
4.7%
avenue 10
 
3.4%
way 6
 
2.0%
5th 6
 
2.0%
street 6
 
2.0%
ne 6
 
2.0%
s 5
 
1.7%
mercer 5
 
1.7%
Other values (146) 176
59.7%
2023-08-01T12:37:53.578272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
211
 
15.8%
e 119
 
8.9%
1 79
 
5.9%
0 75
 
5.6%
t 67
 
5.0%
A 58
 
4.3%
v 56
 
4.2%
r 48
 
3.6%
2 40
 
3.0%
S 39
 
2.9%
Other values (51) 543
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 515
38.6%
Decimal Number 343
25.7%
Uppercase Letter 239
17.9%
Space Separator 211
15.8%
Other Punctuation 26
 
1.9%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 119
23.1%
t 67
13.0%
v 56
10.9%
r 48
9.3%
n 39
 
7.6%
h 27
 
5.2%
i 27
 
5.2%
a 24
 
4.7%
o 20
 
3.9%
s 19
 
3.7%
Other values (13) 69
13.4%
Uppercase Letter
ValueCountFrequency (%)
A 58
24.3%
S 39
16.3%
N 27
11.3%
E 18
 
7.5%
W 14
 
5.9%
T 12
 
5.0%
O 10
 
4.2%
R 9
 
3.8%
M 8
 
3.3%
L 6
 
2.5%
Other values (13) 38
15.9%
Decimal Number
ValueCountFrequency (%)
1 79
23.0%
0 75
21.9%
2 40
11.7%
5 35
10.2%
4 29
 
8.5%
3 28
 
8.2%
6 20
 
5.8%
7 14
 
4.1%
8 13
 
3.8%
9 10
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 24
92.3%
# 1
 
3.8%
& 1
 
3.8%
Space Separator
ValueCountFrequency (%)
211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 754
56.5%
Common 581
43.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 119
15.8%
t 67
 
8.9%
A 58
 
7.7%
v 56
 
7.4%
r 48
 
6.4%
S 39
 
5.2%
n 39
 
5.2%
h 27
 
3.6%
N 27
 
3.6%
i 27
 
3.6%
Other values (36) 247
32.8%
Common
ValueCountFrequency (%)
211
36.3%
1 79
 
13.6%
0 75
 
12.9%
2 40
 
6.9%
5 35
 
6.0%
4 29
 
5.0%
3 28
 
4.8%
. 24
 
4.1%
6 20
 
3.4%
7 14
 
2.4%
Other values (5) 26
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
211
 
15.8%
e 119
 
8.9%
1 79
 
5.9%
0 75
 
5.6%
t 67
 
5.0%
A 58
 
4.3%
v 56
 
4.2%
r 48
 
3.6%
2 40
 
3.0%
S 39
 
2.9%
Other values (51) 543
40.7%

City
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.9 KiB
Seattle
84 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters588
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSeattle
2nd rowSeattle
3rd rowSeattle
4th rowSeattle
5th rowSeattle

Common Values

ValueCountFrequency (%)
Seattle 84
100.0%

Length

2023-08-01T12:37:53.719603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:53.840758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
seattle 84
100.0%

Most occurring characters

ValueCountFrequency (%)
e 168
28.6%
t 168
28.6%
S 84
14.3%
a 84
14.3%
l 84
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 504
85.7%
Uppercase Letter 84
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 168
33.3%
t 168
33.3%
a 84
16.7%
l 84
16.7%
Uppercase Letter
ValueCountFrequency (%)
S 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 588
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 168
28.6%
t 168
28.6%
S 84
14.3%
a 84
14.3%
l 84
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 588
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 168
28.6%
t 168
28.6%
S 84
14.3%
a 84
14.3%
l 84
14.3%

State
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
WA
84 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters168
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 84
100.0%

Length

2023-08-01T12:37:53.941669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:54.070820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wa 84
100.0%

Most occurring characters

ValueCountFrequency (%)
W 84
50.0%
A 84
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 168
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 84
50.0%
A 84
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 168
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 84
50.0%
A 84
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 84
50.0%
A 84
50.0%

ZipCode
Real number (ℝ)

Distinct21
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98108.5
Minimum98033
Maximum98154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:54.163702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98033
5-th percentile98101
Q198101
median98106
Q398109.5
95-th percentile98133
Maximum98154
Range121
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation14.548216
Coefficient of variation (CV)0.00014828701
Kurtosis9.6579559
Mean98108.5
Median Absolute Deviation (MAD)5
Skewness-0.91880605
Sum8241114
Variance211.6506
MonotonicityNot monotonic
2023-08-01T12:37:54.292914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
98101 23
27.4%
98109 18
21.4%
98104 12
14.3%
98105 4
 
4.8%
98121 4
 
4.8%
98133 3
 
3.6%
98122 2
 
2.4%
98115 2
 
2.4%
98112 2
 
2.4%
98107 2
 
2.4%
Other values (11) 12
14.3%
ValueCountFrequency (%)
98033 1
 
1.2%
98070 1
 
1.2%
98101 23
27.4%
98103 1
 
1.2%
98104 12
14.3%
98105 4
 
4.8%
98107 2
 
2.4%
98108 1
 
1.2%
98109 18
21.4%
98111 1
 
1.2%
ValueCountFrequency (%)
98154 1
 
1.2%
98144 2
2.4%
98136 1
 
1.2%
98133 3
3.6%
98126 1
 
1.2%
98122 2
2.4%
98121 4
4.8%
98119 1
 
1.2%
98118 1
 
1.2%
98115 2
2.4%

TaxParcelIdentificationNumber
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.492936 × 109
Minimum22000005
Maximum5.2478015 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:54.436299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22000005
5-th percentile6.2924169 × 108
Q19.2504928 × 108
median1.8617749 × 109
Q31.9852001 × 109
95-th percentile2.3136084 × 109
Maximum5.2478015 × 109
Range5.2258015 × 109
Interquartile range (IQR)1.0601508 × 109

Descriptive statistics

Standard deviation7.7010807 × 108
Coefficient of variation (CV)0.51583463
Kurtosis5.1217813
Mean1.492936 × 109
Median Absolute Deviation (MAD)5.2397412 × 108
Skewness1.1931218
Sum1.2540662 × 1011
Variance5.9306644 × 1017
MonotonicityNot monotonic
2023-08-01T12:37:54.597887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1988200700 2
 
2.4%
1985200003 2
 
2.4%
659000030 1
 
1.2%
2324039001 1
 
1.2%
1983200325 1
 
1.2%
1983200230 1
 
1.2%
1979200290 1
 
1.2%
1979200270 1
 
1.2%
1978201270 1
 
1.2%
2467400430 1
 
1.2%
Other values (72) 72
85.7%
ValueCountFrequency (%)
22000005 1
1.2%
323049024 1
1.2%
467000429 1
1.2%
570000340 1
1.2%
625049002 1
1.2%
653000225 1
1.2%
659000030 1
1.2%
659000220 1
1.2%
659000475 1
1.2%
659000640 1
1.2%
ValueCountFrequency (%)
5247801465 1
1.2%
2767703875 1
1.2%
2467400430 1
1.2%
2426039059 1
1.2%
2324039001 1
1.2%
2254501944 1
1.2%
2249000170 1
1.2%
2125049015 1
1.2%
1992200235 1
1.2%
1991200940 1
1.2%

CouncilDistrictCode
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8571429
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:54.719043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q14
median7
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7844229
Coefficient of variation (CV)0.30465758
Kurtosis-0.18788957
Mean5.8571429
Median Absolute Deviation (MAD)0
Skewness-1.1740361
Sum492
Variance3.1841652
MonotonicityNot monotonic
2023-08-01T12:37:54.820016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 56
66.7%
3 10
 
11.9%
4 7
 
8.3%
2 4
 
4.8%
6 3
 
3.6%
5 3
 
3.6%
1 1
 
1.2%
ValueCountFrequency (%)
1 1
 
1.2%
2 4
 
4.8%
3 10
 
11.9%
4 7
 
8.3%
5 3
 
3.6%
6 3
 
3.6%
7 56
66.7%
ValueCountFrequency (%)
7 56
66.7%
6 3
 
3.6%
5 3
 
3.6%
4 7
 
8.3%
3 10
 
11.9%
2 4
 
4.8%
1 1
 
1.2%

Neighborhood
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
DOWNTOWN
38 
MAGNOLIA / QUEEN ANNE
12 
LAKE UNION
EAST
NORTHEAST
Other values (6)
12 

Length

Max length21
Median length16
Mean length10.130952
Min length4

Characters and Unicode

Total characters851
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.6%

Sample

1st rowDOWNTOWN
2nd rowDOWNTOWN
3rd rowDOWNTOWN
4th rowDOWNTOWN
5th rowDOWNTOWN

Common Values

ValueCountFrequency (%)
DOWNTOWN 38
45.2%
MAGNOLIA / QUEEN ANNE 12
 
14.3%
LAKE UNION 9
 
10.7%
EAST 7
 
8.3%
NORTHEAST 6
 
7.1%
NORTHWEST 4
 
4.8%
GREATER DUWAMISH 3
 
3.6%
BALLARD 2
 
2.4%
CENTRAL 1
 
1.2%
SOUTHWEST 1
 
1.2%

Length

2023-08-01T12:37:54.951287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
downtown 38
28.8%
magnolia 12
 
9.1%
12
 
9.1%
queen 12
 
9.1%
anne 12
 
9.1%
lake 9
 
6.8%
union 9
 
6.8%
east 7
 
5.3%
northeast 6
 
4.5%
northwest 4
 
3.0%
Other values (6) 11
 
8.3%

Most occurring characters

ValueCountFrequency (%)
N 153
18.0%
O 109
12.8%
W 84
9.9%
T 73
8.6%
E 71
8.3%
A 70
8.2%
48
 
5.6%
D 43
 
5.1%
L 26
 
3.1%
U 26
 
3.1%
Other values (11) 148
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 791
92.9%
Space Separator 48
 
5.6%
Other Punctuation 12
 
1.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 153
19.3%
O 109
13.8%
W 84
10.6%
T 73
9.2%
E 71
9.0%
A 70
8.8%
D 43
 
5.4%
L 26
 
3.3%
U 26
 
3.3%
I 24
 
3.0%
Other values (9) 112
14.2%
Space Separator
ValueCountFrequency (%)
48
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 791
92.9%
Common 60
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 153
19.3%
O 109
13.8%
W 84
10.6%
T 73
9.2%
E 71
9.0%
A 70
8.8%
D 43
 
5.4%
L 26
 
3.3%
U 26
 
3.3%
I 24
 
3.0%
Other values (9) 112
14.2%
Common
ValueCountFrequency (%)
48
80.0%
/ 12
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 851
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 153
18.0%
O 109
12.8%
W 84
9.9%
T 73
8.6%
E 71
8.3%
A 70
8.2%
48
 
5.6%
D 43
 
5.1%
L 26
 
3.1%
U 26
 
3.1%
Other values (11) 148
17.4%

Latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.621343
Minimum47.51138
Maximum47.73141
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:55.092547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.51138
5-th percentile47.586636
Q147.608195
median47.614025
Q347.62395
95-th percentile47.675537
Maximum47.73141
Range0.22003
Interquartile range (IQR)0.015755

Descriptive statistics

Standard deviation0.032855604
Coefficient of variation (CV)0.00068993443
Kurtosis4.231867
Mean47.621343
Median Absolute Deviation (MAD)0.007795
Skewness0.78890496
Sum4000.1928
Variance0.0010794907
MonotonicityNot monotonic
2023-08-01T12:37:55.244223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.62208 3
 
3.6%
47.62395 3
 
3.6%
47.6122 1
 
1.2%
47.60823 1
 
1.2%
47.60845 1
 
1.2%
47.60898 1
 
1.2%
47.61244 1
 
1.2%
47.61988 1
 
1.2%
47.72426 1
 
1.2%
47.55837 1
 
1.2%
Other values (70) 70
83.3%
ValueCountFrequency (%)
47.51138 1
1.2%
47.53493 1
1.2%
47.55837 1
1.2%
47.58034 1
1.2%
47.58408 1
1.2%
47.60112 1
1.2%
47.60265 1
1.2%
47.60294 1
1.2%
47.60376 1
1.2%
47.60378 1
1.2%
ValueCountFrequency (%)
47.73141 1
1.2%
47.72551 1
1.2%
47.72426 1
1.2%
47.68891 1
1.2%
47.67559 1
1.2%
47.67524 1
1.2%
47.66737 1
1.2%
47.66587 1
1.2%
47.66583 1
1.2%
47.6641 1
1.2%

Longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.33491
Minimum-122.38476
Maximum-122.28875
Zeros0
Zeros (%)0.0%
Negative84
Negative (%)100.0%
Memory size1.3 KiB
2023-08-01T12:37:55.395651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.38476
5-th percentile-122.35822
Q1-122.34241
median-122.33423
Q3-122.3303
95-th percentile-122.30484
Maximum-122.28875
Range0.09601
Interquartile range (IQR)0.0121125

Descriptive statistics

Standard deviation0.01675512
Coefficient of variation (CV)-0.00013696106
Kurtosis1.5168745
Mean-122.33491
Median Absolute Deviation (MAD)0.006645
Skewness-0.041725698
Sum-10276.132
Variance0.00028073403
MonotonicityNot monotonic
2023-08-01T12:37:55.557144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.35398 3
 
3.6%
-122.33629 2
 
2.4%
-122.35077 2
 
2.4%
-122.37751 1
 
1.2%
-122.33805 1
 
1.2%
-122.32637 1
 
1.2%
-122.32693 1
 
1.2%
-122.3214 1
 
1.2%
-122.33211 1
 
1.2%
-122.35599 1
 
1.2%
Other values (70) 70
83.3%
ValueCountFrequency (%)
-122.38476 1
 
1.2%
-122.37956 1
 
1.2%
-122.37751 1
 
1.2%
-122.35951 1
 
1.2%
-122.35842 1
 
1.2%
-122.35708 1
 
1.2%
-122.35599 1
 
1.2%
-122.35398 3
3.6%
-122.35143 1
 
1.2%
-122.35129 1
 
1.2%
ValueCountFrequency (%)
-122.28875 1
1.2%
-122.29598 1
1.2%
-122.29965 1
1.2%
-122.30125 1
1.2%
-122.30429 1
1.2%
-122.30793 1
1.2%
-122.30828 1
1.2%
-122.30956 1
1.2%
-122.31132 1
1.2%
-122.31534 1
1.2%

YearBuilt
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1959.0119
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:55.708149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1907.15
Q11926.75
median1962
Q31992.5
95-th percentile2005.7
Maximum2010
Range110
Interquartile range (IQR)65.75

Descriptive statistics

Standard deviation34.924874
Coefficient of variation (CV)0.017827801
Kurtosis-1.4410213
Mean1959.0119
Median Absolute Deviation (MAD)34
Skewness-0.12891934
Sum164557
Variance1219.7468
MonotonicityNot monotonic
2023-08-01T12:37:55.859306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
1962 5
 
6.0%
1926 4
 
4.8%
1928 4
 
4.8%
2001 3
 
3.6%
1969 3
 
3.6%
2010 3
 
3.6%
1998 3
 
3.6%
1908 3
 
3.6%
1930 3
 
3.6%
1985 2
 
2.4%
Other values (39) 51
60.7%
ValueCountFrequency (%)
1900 1
 
1.2%
1904 2
2.4%
1906 1
 
1.2%
1907 1
 
1.2%
1908 3
3.6%
1910 1
 
1.2%
1911 2
2.4%
1916 2
2.4%
1920 1
 
1.2%
1922 2
2.4%
ValueCountFrequency (%)
2010 3
3.6%
2008 1
 
1.2%
2006 1
 
1.2%
2004 1
 
1.2%
2003 1
 
1.2%
2002 2
2.4%
2001 3
3.6%
2000 1
 
1.2%
1999 2
2.4%
1998 3
3.6%

NumberofBuildings
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1.0
79 
0.0
 
2
3.0
 
1
2.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters252
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)3.6%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 79
94.0%
0.0 2
 
2.4%
3.0 1
 
1.2%
2.0 1
 
1.2%
4.0 1
 
1.2%

Length

2023-08-01T12:37:56.010524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:56.161550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 79
94.0%
0.0 2
 
2.4%
3.0 1
 
1.2%
2.0 1
 
1.2%
4.0 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 86
34.1%
. 84
33.3%
1 79
31.3%
3 1
 
0.4%
2 1
 
0.4%
4 1
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 168
66.7%
Other Punctuation 84
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 86
51.2%
1 79
47.0%
3 1
 
0.6%
2 1
 
0.6%
4 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 86
34.1%
. 84
33.3%
1 79
31.3%
3 1
 
0.4%
2 1
 
0.4%
4 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 86
34.1%
. 84
33.3%
1 79
31.3%
3 1
 
0.4%
2 1
 
0.4%
4 1
 
0.4%

NumberofFloors
Real number (ℝ)

Distinct22
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6666667
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:56.282654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median5
Q39.25
95-th percentile24.85
Maximum41
Range40
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation7.7495951
Coefficient of variation (CV)1.0108168
Kurtosis6.0184078
Mean7.6666667
Median Absolute Deviation (MAD)3
Skewness2.3777032
Sum644
Variance60.056225
MonotonicityNot monotonic
2023-08-01T12:37:56.413887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 15
17.9%
6 10
11.9%
3 9
10.7%
5 9
10.7%
4 7
8.3%
7 6
 
7.1%
11 5
 
6.0%
10 4
 
4.8%
1 3
 
3.6%
8 3
 
3.6%
Other values (12) 13
15.5%
ValueCountFrequency (%)
1 3
 
3.6%
2 15
17.9%
3 9
10.7%
4 7
8.3%
5 9
10.7%
6 10
11.9%
7 6
 
7.1%
8 3
 
3.6%
9 1
 
1.2%
10 4
 
4.8%
ValueCountFrequency (%)
41 1
1.2%
34 1
1.2%
33 1
1.2%
28 1
1.2%
25 1
1.2%
24 1
1.2%
20 1
1.2%
19 1
1.2%
18 1
1.2%
15 1
1.2%

PropertyGFATotal
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191752.83
Minimum50017
Maximum994212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:56.575413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50017
5-th percentile52918.5
Q170224
median103738.5
Q3209601.25
95-th percentile566683.65
Maximum994212
Range944195
Interquartile range (IQR)139377.25

Descriptive statistics

Standard deviation201063.79
Coefficient of variation (CV)1.0485571
Kurtosis6.215364
Mean191752.83
Median Absolute Deviation (MAD)46512.5
Skewness2.4284211
Sum16107238
Variance4.0426649 × 1010
MonotonicityNot monotonic
2023-08-01T12:37:56.726855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88434 1
 
1.2%
93397 1
 
1.2%
389000 1
 
1.2%
282863 1
 
1.2%
316306 1
 
1.2%
58320 1
 
1.2%
99780 1
 
1.2%
76631 1
 
1.2%
179014 1
 
1.2%
84103 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
50017 1
1.2%
51390 1
1.2%
52000 1
1.2%
52549 1
1.2%
52554 1
1.2%
54984 1
1.2%
56072 1
1.2%
56521 1
1.2%
57428 1
1.2%
57452 1
1.2%
ValueCountFrequency (%)
994212 1
1.2%
956110 1
1.2%
920598 1
1.2%
714095 1
1.2%
571329 1
1.2%
540360 1
1.2%
494835 1
1.2%
416281 1
1.2%
412000 1
1.2%
396085 1
1.2%

PropertyGFAParking
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)36.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20963.393
Minimum0
Maximum303707
Zeros54
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:56.868171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326497.5
95-th percentile81550
Maximum303707
Range303707
Interquartile range (IQR)26497.5

Descriptive statistics

Standard deviation48923.152
Coefficient of variation (CV)2.3337421
Kurtosis16.793066
Mean20963.393
Median Absolute Deviation (MAD)0
Skewness3.836682
Sum1760925
Variance2.3934748 × 109
MonotonicityNot monotonic
2023-08-01T12:37:56.997393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 54
64.3%
10043 1
 
1.2%
36934 1
 
1.2%
25930 1
 
1.2%
303707 1
 
1.2%
2352 1
 
1.2%
44766 1
 
1.2%
9341 1
 
1.2%
20732 1
 
1.2%
44891 1
 
1.2%
Other values (21) 21
 
25.0%
ValueCountFrequency (%)
0 54
64.3%
2352 1
 
1.2%
9341 1
 
1.2%
10043 1
 
1.2%
15064 1
 
1.2%
16200 1
 
1.2%
19279 1
 
1.2%
20732 1
 
1.2%
25200 1
 
1.2%
25930 1
 
1.2%
ValueCountFrequency (%)
303707 1
1.2%
205970 1
1.2%
196718 1
1.2%
146694 1
1.2%
85000 1
1.2%
62000 1
1.2%
61161 1
1.2%
57600 1
1.2%
57000 1
1.2%
44891 1
1.2%

PropertyGFABuilding(s)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170789.44
Minimum50017
Maximum847518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:57.148980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50017
5-th percentile52260.85
Q163603.5
median92305
Q3197311.5
95-th percentile566683.65
Maximum847518
Range797501
Interquartile range (IQR)133708

Descriptive statistics

Standard deviation173455.92
Coefficient of variation (CV)1.0156127
Kurtosis4.4322991
Mean170789.44
Median Absolute Deviation (MAD)34576
Skewness2.153217
Sum14346313
Variance3.0086955 × 1010
MonotonicityNot monotonic
2023-08-01T12:37:57.302680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88434 1
 
1.2%
93397 1
 
1.2%
389000 1
 
1.2%
238097 1
 
1.2%
316306 1
 
1.2%
58320 1
 
1.2%
90439 1
 
1.2%
76631 1
 
1.2%
179014 1
 
1.2%
63371 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
50017 1
1.2%
50283 1
1.2%
51390 1
1.2%
52000 1
1.2%
52210 1
1.2%
52549 1
1.2%
52554 1
1.2%
54984 1
1.2%
56072 1
1.2%
56521 1
1.2%
ValueCountFrequency (%)
847518 1
1.2%
759392 1
1.2%
714095 1
1.2%
616891 1
1.2%
571329 1
1.2%
540360 1
1.2%
494835 1
1.2%
396085 1
1.2%
389000 1
1.2%
385274 1
1.2%
Distinct42
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
2023-08-01T12:37:57.494437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length100
Median length75
Mean length22.011905
Min length5

Characters and Unicode

Total characters1849
Distinct characters51
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)40.5%

Sample

1st rowHotel
2nd rowHotel, Parking, Restaurant
3rd rowHotel
4th rowHotel
5th rowHotel, Parking, Swimming Pool
ValueCountFrequency (%)
hotel 37
15.6%
parking 28
 
11.8%
other 24
 
10.1%
office 22
 
9.3%
9
 
3.8%
retail 8
 
3.4%
store 8
 
3.4%
school 6
 
2.5%
swimming 5
 
2.1%
pool 5
 
2.1%
Other values (47) 85
35.9%
2023-08-01T12:37:57.825081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 192
 
10.4%
153
 
8.3%
t 147
 
8.0%
i 122
 
6.6%
r 114
 
6.2%
a 104
 
5.6%
o 102
 
5.5%
l 97
 
5.2%
n 87
 
4.7%
, 80
 
4.3%
Other values (41) 651
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1323
71.6%
Uppercase Letter 246
 
13.3%
Space Separator 153
 
8.3%
Other Punctuation 94
 
5.1%
Dash Punctuation 19
 
1.0%
Decimal Number 10
 
0.5%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 192
14.5%
t 147
11.1%
i 122
9.2%
r 114
8.6%
a 104
7.9%
o 102
 
7.7%
l 97
 
7.3%
n 87
 
6.6%
f 53
 
4.0%
g 49
 
3.7%
Other values (12) 256
19.3%
Uppercase Letter
ValueCountFrequency (%)
O 46
18.7%
H 42
17.1%
P 42
17.1%
S 30
12.2%
R 19
7.7%
C 14
 
5.7%
D 7
 
2.8%
M 7
 
2.8%
E 7
 
2.8%
A 6
 
2.4%
Other values (10) 26
10.6%
Other Punctuation
ValueCountFrequency (%)
, 80
85.1%
/ 13
 
13.8%
& 1
 
1.1%
Decimal Number
ValueCountFrequency (%)
2 5
50.0%
1 5
50.0%
Space Separator
ValueCountFrequency (%)
153
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1569
84.9%
Common 280
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 192
 
12.2%
t 147
 
9.4%
i 122
 
7.8%
r 114
 
7.3%
a 104
 
6.6%
o 102
 
6.5%
l 97
 
6.2%
n 87
 
5.5%
f 53
 
3.4%
g 49
 
3.1%
Other values (32) 502
32.0%
Common
ValueCountFrequency (%)
153
54.6%
, 80
28.6%
- 19
 
6.8%
/ 13
 
4.6%
2 5
 
1.8%
1 5
 
1.8%
) 2
 
0.7%
( 2
 
0.7%
& 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1849
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 192
 
10.4%
153
 
8.3%
t 147
 
8.0%
i 122
 
6.6%
r 114
 
6.2%
a 104
 
5.6%
o 102
 
5.5%
l 97
 
5.2%
n 87
 
4.7%
, 80
 
4.3%
Other values (41) 651
35.2%

LargestPropertyUseType
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Hotel
37 
Office
13 
Other
10 
K-12 School
Other - Entertainment/Public Assembly
 
3
Other values (14)
17 

Length

Max length37
Median length5
Mean length9.3571429
Min length5

Characters and Unicode

Total characters786
Distinct characters45
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)13.1%

Sample

1st rowHotel
2nd rowHotel
3rd rowHotel
4th rowHotel
5th rowHotel

Common Values

ValueCountFrequency (%)
Hotel 37
44.0%
Office 13
 
15.5%
Other 10
 
11.9%
K-12 School 4
 
4.8%
Other - Entertainment/Public Assembly 3
 
3.6%
Self-Storage Facility 2
 
2.4%
Medical Office 2
 
2.4%
Senior Care Community 2
 
2.4%
College/University 1
 
1.2%
Library 1
 
1.2%
Other values (9) 9
 
10.7%

Length

2023-08-01T12:37:57.978482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hotel 37
31.4%
office 15
12.7%
other 14
 
11.9%
5
 
4.2%
k-12 4
 
3.4%
school 4
 
3.4%
entertainment/public 3
 
2.5%
assembly 3
 
2.5%
medical 3
 
2.5%
care 2
 
1.7%
Other values (24) 28
23.7%

Most occurring characters

ValueCountFrequency (%)
e 109
13.9%
t 79
 
10.1%
l 70
 
8.9%
o 63
 
8.0%
i 46
 
5.9%
H 40
 
5.1%
r 35
 
4.5%
34
 
4.3%
f 33
 
4.2%
c 30
 
3.8%
Other values (35) 247
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 600
76.3%
Uppercase Letter 123
 
15.6%
Space Separator 34
 
4.3%
Dash Punctuation 11
 
1.4%
Other Punctuation 8
 
1.0%
Decimal Number 8
 
1.0%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 109
18.2%
t 79
13.2%
l 70
11.7%
o 63
10.5%
i 46
7.7%
r 35
 
5.8%
f 33
 
5.5%
c 30
 
5.0%
a 25
 
4.2%
h 22
 
3.7%
Other values (10) 88
14.7%
Uppercase Letter
ValueCountFrequency (%)
H 40
32.5%
O 29
23.6%
S 14
 
11.4%
C 8
 
6.5%
M 5
 
4.1%
P 4
 
3.3%
A 4
 
3.3%
K 4
 
3.3%
E 3
 
2.4%
F 3
 
2.4%
Other values (7) 9
 
7.3%
Other Punctuation
ValueCountFrequency (%)
/ 7
87.5%
& 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
2 4
50.0%
1 4
50.0%
Space Separator
ValueCountFrequency (%)
34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 723
92.0%
Common 63
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 109
15.1%
t 79
10.9%
l 70
 
9.7%
o 63
 
8.7%
i 46
 
6.4%
H 40
 
5.5%
r 35
 
4.8%
f 33
 
4.6%
c 30
 
4.1%
O 29
 
4.0%
Other values (27) 189
26.1%
Common
ValueCountFrequency (%)
34
54.0%
- 11
 
17.5%
/ 7
 
11.1%
2 4
 
6.3%
1 4
 
6.3%
( 1
 
1.6%
& 1
 
1.6%
) 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 109
13.9%
t 79
 
10.1%
l 70
 
8.9%
o 63
 
8.0%
i 46
 
5.9%
H 40
 
5.1%
r 35
 
4.5%
34
 
4.3%
f 33
 
4.2%
c 30
 
3.8%
Other values (35) 247
31.4%

LargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171656.29
Minimum16442
Maximum994212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:58.129937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum16442
5-th percentile38699.25
Q157522.5
median88462
Q3191761
95-th percentile613420.15
Maximum994212
Range977770
Interquartile range (IQR)134238.5

Descriptive statistics

Standard deviation193401.61
Coefficient of variation (CV)1.1266795
Kurtosis5.1868947
Mean171656.29
Median Absolute Deviation (MAD)38953.5
Skewness2.2609204
Sum14419128
Variance3.7404183 × 1010
MonotonicityNot monotonic
2023-08-01T12:37:58.291497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88434 1
 
1.2%
93397 1
 
1.2%
368000 1
 
1.2%
235788 1
 
1.2%
261826 1
 
1.2%
72072 1
 
1.2%
40174 1
 
1.2%
76631 1
 
1.2%
179014 1
 
1.2%
63371 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
16442 1
1.2%
23500 1
1.2%
26225 1
1.2%
34947 1
1.2%
38439 1
1.2%
40174 1
1.2%
40943 1
1.2%
41340 1
1.2%
42755 1
1.2%
45900 1
1.2%
ValueCountFrequency (%)
994212 1
1.2%
757027 1
1.2%
756493 1
1.2%
729584 1
1.2%
616000 1
1.2%
598801 1
1.2%
537150 1
1.2%
385274 1
1.2%
368000 1
1.2%
364913 1
1.2%

SecondLargestPropertyUseType
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)31.1%
Missing39
Missing (%)46.4%
Memory size4.8 KiB
Parking
28 
Restaurant
Office
 
2
Retail Store
 
2
K-12 School
 
1
Other values (9)

Length

Max length52
Median length7
Mean length10.622222
Min length6

Characters and Unicode

Total characters478
Distinct characters44
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)22.2%

Sample

1st rowParking
2nd rowParking
3rd rowParking
4th rowParking
5th rowParking

Common Values

ValueCountFrequency (%)
Parking 28
33.3%
Restaurant 3
 
3.6%
Office 2
 
2.4%
Retail Store 2
 
2.4%
K-12 School 1
 
1.2%
Laboratory 1
 
1.2%
Refrigerated Warehouse 1
 
1.2%
Non-Refrigerated Warehouse 1
 
1.2%
Other - Education 1
 
1.2%
Vocational School 1
 
1.2%
Other values (4) 4
 
4.8%
(Missing) 39
46.4%

Length

2023-08-01T12:37:58.442862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
parking 28
44.4%
restaurant 3
 
4.8%
other 2
 
3.2%
office 2
 
3.2%
retail 2
 
3.2%
store 2
 
3.2%
school 2
 
3.2%
warehouse 2
 
3.2%
2
 
3.2%
etc 1
 
1.6%
Other values (17) 17
27.0%

Most occurring characters

ValueCountFrequency (%)
a 52
10.9%
r 48
 
10.0%
i 42
 
8.8%
n 42
 
8.8%
e 33
 
6.9%
g 32
 
6.7%
P 31
 
6.5%
k 28
 
5.9%
t 25
 
5.2%
18
 
3.8%
Other values (34) 127
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 385
80.5%
Uppercase Letter 63
 
13.2%
Space Separator 18
 
3.8%
Dash Punctuation 4
 
0.8%
Other Punctuation 4
 
0.8%
Decimal Number 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 52
13.5%
r 48
12.5%
i 42
10.9%
n 42
10.9%
e 33
8.6%
g 32
8.3%
k 28
7.3%
t 25
6.5%
o 17
 
4.4%
l 11
 
2.9%
Other values (11) 55
14.3%
Uppercase Letter
ValueCountFrequency (%)
P 31
49.2%
R 7
 
11.1%
S 6
 
9.5%
O 4
 
6.3%
W 2
 
3.2%
C 2
 
3.2%
E 2
 
3.2%
D 2
 
3.2%
A 1
 
1.6%
B 1
 
1.6%
Other values (5) 5
 
7.9%
Other Punctuation
ValueCountFrequency (%)
/ 2
50.0%
, 2
50.0%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 448
93.7%
Common 30
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 52
11.6%
r 48
10.7%
i 42
9.4%
n 42
9.4%
e 33
 
7.4%
g 32
 
7.1%
P 31
 
6.9%
k 28
 
6.2%
t 25
 
5.6%
o 17
 
3.8%
Other values (26) 98
21.9%
Common
ValueCountFrequency (%)
18
60.0%
- 4
 
13.3%
/ 2
 
6.7%
, 2
 
6.7%
) 1
 
3.3%
2 1
 
3.3%
( 1
 
3.3%
1 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 52
10.9%
r 48
 
10.0%
i 42
 
8.8%
n 42
 
8.8%
e 33
 
6.9%
g 32
 
6.7%
P 31
 
6.5%
k 28
 
5.9%
t 25
 
5.2%
18
 
3.8%
Other values (34) 127
26.6%

SecondLargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct42
Distinct (%)93.3%
Missing39
Missing (%)46.4%
Infinite0
Infinite (%)0.0%
Mean63553.733
Minimum0
Maximum639931
Zeros4
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:58.574029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115064
median28300
Q351537
95-th percentile257802.8
Maximum639931
Range639931
Interquartile range (IQR)36473

Descriptive statistics

Standard deviation110529.23
Coefficient of variation (CV)1.7391462
Kurtosis17.143938
Mean63553.733
Median Absolute Deviation (MAD)18257
Skewness3.805386
Sum2859918
Variance1.2216711 × 1010
MonotonicityNot monotonic
2023-08-01T12:37:58.715275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 4
 
4.8%
150726 1
 
1.2%
26450 1
 
1.2%
42546 1
 
1.2%
3000 1
 
1.2%
28300 1
 
1.2%
44891 1
 
1.2%
20732 1
 
1.2%
20868 1
 
1.2%
51537 1
 
1.2%
Other values (32) 32
38.1%
(Missing) 39
46.4%
ValueCountFrequency (%)
0 4
4.8%
3000 1
 
1.2%
4993 1
 
1.2%
5181 1
 
1.2%
5681 1
 
1.2%
7849 1
 
1.2%
10043 1
 
1.2%
13730 1
 
1.2%
15064 1
 
1.2%
15505 1
 
1.2%
ValueCountFrequency (%)
639931 1
1.2%
310699 1
1.2%
276000 1
1.2%
185014 1
1.2%
150726 1
1.2%
148865 1
1.2%
117668 1
1.2%
85000 1
1.2%
68009 1
1.2%
65676 1
1.2%

ThirdLargestPropertyUseType
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)40.9%
Missing62
Missing (%)73.8%
Memory size4.1 KiB
Office
Other
Swimming Pool
Retail Store
Restaurant
Other values (4)

Length

Max length37
Median length19
Mean length11.318182
Min length5

Characters and Unicode

Total characters249
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st rowRestaurant
2nd rowSwimming Pool
3rd rowData Center
4th rowSwimming Pool
5th rowOffice

Common Values

ValueCountFrequency (%)
Office 5
 
6.0%
Other 4
 
4.8%
Swimming Pool 3
 
3.6%
Retail Store 3
 
3.6%
Restaurant 2
 
2.4%
Data Center 2
 
2.4%
Other - Entertainment/Public Assembly 1
 
1.2%
Non-Refrigerated Warehouse 1
 
1.2%
Distribution Center 1
 
1.2%
(Missing) 62
73.8%

Length

2023-08-01T12:37:58.866643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:37:59.028168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
office 5
14.3%
other 5
14.3%
swimming 3
8.6%
pool 3
8.6%
retail 3
8.6%
store 3
8.6%
center 3
8.6%
restaurant 2
 
5.7%
data 2
 
5.7%
1
 
2.9%
Other values (5) 5
14.3%

Most occurring characters

ValueCountFrequency (%)
e 32
 
12.9%
t 26
 
10.4%
i 20
 
8.0%
r 18
 
7.2%
a 14
 
5.6%
n 13
 
5.2%
13
 
5.2%
o 12
 
4.8%
f 11
 
4.4%
O 10
 
4.0%
Other values (22) 80
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 197
79.1%
Uppercase Letter 36
 
14.5%
Space Separator 13
 
5.2%
Dash Punctuation 2
 
0.8%
Other Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 32
16.2%
t 26
13.2%
i 20
10.2%
r 18
9.1%
a 14
 
7.1%
n 13
 
6.6%
o 12
 
6.1%
f 11
 
5.6%
l 8
 
4.1%
m 8
 
4.1%
Other values (9) 35
17.8%
Uppercase Letter
ValueCountFrequency (%)
O 10
27.8%
S 6
16.7%
R 6
16.7%
P 4
 
11.1%
C 3
 
8.3%
D 3
 
8.3%
E 1
 
2.8%
A 1
 
2.8%
N 1
 
2.8%
W 1
 
2.8%
Space Separator
ValueCountFrequency (%)
13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233
93.6%
Common 16
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 32
13.7%
t 26
 
11.2%
i 20
 
8.6%
r 18
 
7.7%
a 14
 
6.0%
n 13
 
5.6%
o 12
 
5.2%
f 11
 
4.7%
O 10
 
4.3%
l 8
 
3.4%
Other values (19) 69
29.6%
Common
ValueCountFrequency (%)
13
81.2%
- 2
 
12.5%
/ 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 249
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 32
 
12.9%
t 26
 
10.4%
i 20
 
8.0%
r 18
 
7.2%
a 14
 
5.6%
n 13
 
5.2%
13
 
5.2%
o 12
 
4.8%
f 11
 
4.4%
O 10
 
4.0%
Other values (22) 80
32.1%

ThirdLargestPropertyUseTypeGFA
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct20
Distinct (%)90.9%
Missing62
Missing (%)73.8%
Infinite0
Infinite (%)0.0%
Mean29942.091
Minimum0
Maximum459748
Zeros3
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:59.199956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12769.25
median4811
Q39706.75
95-th percentile68760.15
Maximum459748
Range459748
Interquartile range (IQR)6937.5

Descriptive statistics

Standard deviation97183.169
Coefficient of variation (CV)3.2457041
Kurtosis20.785339
Mean29942.091
Median Absolute Deviation (MAD)4027.5
Skewness4.5174298
Sum658726
Variance9.4445682 × 109
MonotonicityNot monotonic
2023-08-01T12:37:59.321076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 3
 
3.6%
5459 1
 
1.2%
6000 1
 
1.2%
4551 1
 
1.2%
26203 1
 
1.2%
15139 1
 
1.2%
17020 1
 
1.2%
8000 1
 
1.2%
9604 1
 
1.2%
5000 1
 
1.2%
Other values (10) 10
 
11.9%
(Missing) 62
73.8%
ValueCountFrequency (%)
0 3
3.6%
493 1
 
1.2%
1074 1
 
1.2%
2406 1
 
1.2%
3859 1
 
1.2%
4307 1
 
1.2%
4500 1
 
1.2%
4551 1
 
1.2%
4622 1
 
1.2%
5000 1
 
1.2%
ValueCountFrequency (%)
459748 1
1.2%
71000 1
1.2%
26203 1
1.2%
17020 1
1.2%
15139 1
1.2%
9741 1
1.2%
9604 1
1.2%
8000 1
1.2%
6000 1
1.2%
5459 1
1.2%
Distinct2
Distinct (%)100.0%
Missing82
Missing (%)97.6%
Memory size1.3 KiB
Minimum2014-01-01 00:00:00
Maximum2016-01-01 00:00:00
2023-08-01T12:37:59.422024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:59.512794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

ENERGYSTARScore
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)75.4%
Missing27
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean62.526316
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:59.662146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q143
median63
Q383
95-th percentile98
Maximum100
Range99
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.652976
Coefficient of variation (CV)0.41027487
Kurtosis-0.55311757
Mean62.526316
Median Absolute Deviation (MAD)20
Skewness-0.45637679
Sum3564
Variance658.07519
MonotonicityNot monotonic
2023-08-01T12:37:59.805558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
71 3
 
3.6%
57 3
 
3.6%
78 2
 
2.4%
48 2
 
2.4%
63 2
 
2.4%
14 2
 
2.4%
84 2
 
2.4%
76 2
 
2.4%
96 2
 
2.4%
98 2
 
2.4%
Other values (33) 35
41.7%
(Missing) 27
32.1%
ValueCountFrequency (%)
1 1
1.2%
8 1
1.2%
14 2
2.4%
27 1
1.2%
29 1
1.2%
30 1
1.2%
31 1
1.2%
34 1
1.2%
35 1
1.2%
36 1
1.2%
ValueCountFrequency (%)
100 1
1.2%
99 1
1.2%
98 2
2.4%
96 2
2.4%
95 1
1.2%
94 1
1.2%
93 1
1.2%
91 1
1.2%
89 1
1.2%
86 1
1.2%

SiteEUI(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.311905
Minimum8.1000004
Maximum286.39999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:37:59.954962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.1000004
5-th percentile23.035001
Q152.000001
median75.049999
Q395.100002
95-th percentile173.18501
Maximum286.39999
Range278.29999
Interquartile range (IQR)43.100001

Descriptive statistics

Standard deviation47.922662
Coefficient of variation (CV)0.58220815
Kurtosis4.4191547
Mean82.311905
Median Absolute Deviation (MAD)21.100002
Skewness1.7340409
Sum6914.2
Variance2296.5815
MonotonicityNot monotonic
2023-08-01T12:38:00.098356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.69999695 2
 
2.4%
46.40000153 2
 
2.4%
81.69999695 1
 
1.2%
58.5 1
 
1.2%
64.30000305 1
 
1.2%
82.5 1
 
1.2%
48 1
 
1.2%
14.60000038 1
 
1.2%
204.5 1
 
1.2%
94.69999695 1
 
1.2%
Other values (72) 72
85.7%
ValueCountFrequency (%)
8.100000381 1
1.2%
12.10000038 1
1.2%
14.60000038 1
1.2%
19.5 1
1.2%
21.70000076 1
1.2%
30.60000038 1
1.2%
32 1
1.2%
34.09999847 1
1.2%
35.70000076 1
1.2%
39.09999847 1
1.2%
ValueCountFrequency (%)
286.3999939 1
1.2%
225 1
1.2%
221.6999969 1
1.2%
204.5 1
1.2%
175.6000061 1
1.2%
159.5 1
1.2%
144.3999939 1
1.2%
136.1000061 1
1.2%
132.3999939 1
1.2%
130.1000061 1
1.2%

SiteEUIWN(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.942857
Minimum8.1000004
Maximum299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:00.259820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.1000004
5-th percentile23.325
Q153.075
median76.25
Q397.549999
95-th percentile164.61
Maximum299
Range290.9
Interquartile range (IQR)44.474999

Descriptive statistics

Standard deviation47.430818
Coefficient of variation (CV)0.56503698
Kurtosis5.2186145
Mean83.942857
Median Absolute Deviation (MAD)21.5
Skewness1.7856637
Sum7051.2
Variance2249.6825
MonotonicityNot monotonic
2023-08-01T12:38:00.401160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.40000153 2
 
2.4%
84.30000305 1
 
1.2%
181.6000061 1
 
1.2%
85.59999847 1
 
1.2%
48.40000153 1
 
1.2%
17.20000076 1
 
1.2%
207.3000031 1
 
1.2%
94.59999847 1
 
1.2%
92.30000305 1
 
1.2%
59.79999924 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
8.100000381 1
1.2%
12.39999962 1
1.2%
17.20000076 1
1.2%
19.5 1
1.2%
21.60000038 1
1.2%
33.09999847 1
1.2%
34.09999847 1
1.2%
36.79999924 1
1.2%
37.09999847 1
1.2%
44 1
1.2%
ValueCountFrequency (%)
299 1
1.2%
233.1000061 1
1.2%
207.3000031 1
1.2%
181.6000061 1
1.2%
165.8999939 1
1.2%
157.3000031 1
1.2%
153.6999969 1
1.2%
141.6000061 1
1.2%
137.1000061 1
1.2%
132.6999969 1
1.2%

SourceEUI(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION 

Distinct83
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.89643
Minimum25.5
Maximum505.79999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:00.552536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile54.6
Q1130.025
median171.6
Q3215.97499
95-th percentile397.12501
Maximum505.79999
Range480.29999
Interquartile range (IQR)85.949997

Descriptive statistics

Standard deviation98.949999
Coefficient of variation (CV)0.54399089
Kurtosis2.1956747
Mean181.89643
Median Absolute Deviation (MAD)43.949997
Skewness1.285839
Sum15279.3
Variance9791.1023
MonotonicityNot monotonic
2023-08-01T12:38:00.703991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182.5 2
 
2.4%
138.1000061 1
 
1.2%
151.6000061 1
 
1.2%
184 1
 
1.2%
144 1
 
1.2%
25.79999924 1
 
1.2%
505.7999878 1
 
1.2%
172 1
 
1.2%
213.3999939 1
 
1.2%
154.1999969 1
 
1.2%
Other values (73) 73
86.9%
ValueCountFrequency (%)
25.5 1
1.2%
25.79999924 1
1.2%
35.20000076 1
1.2%
48.79999924 1
1.2%
54 1
1.2%
58 1
1.2%
61.20000076 1
1.2%
68.19999695 1
1.2%
72.59999847 1
1.2%
74 1
1.2%
ValueCountFrequency (%)
505.7999878 1
1.2%
485.2999878 1
1.2%
479.5 1
1.2%
407.3999939 1
1.2%
398.7000122 1
1.2%
388.2000122 1
1.2%
338.2999878 1
1.2%
316.2999878 1
1.2%
311.5 1
1.2%
297.3999939 1
1.2%

SourceEUIWN(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182.42024
Minimum25.5
Maximum504.29999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:00.853359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile58.994999
Q1130.625
median173.65
Q3217.925
95-th percentile390.81999
Maximum504.29999
Range478.79999
Interquartile range (IQR)87.299999

Descriptive statistics

Standard deviation94.516876
Coefficient of variation (CV)0.51812714
Kurtosis2.2938962
Mean182.42024
Median Absolute Deviation (MAD)43.300003
Skewness1.2180199
Sum15323.3
Variance8933.4399
MonotonicityNot monotonic
2023-08-01T12:38:01.007001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189 1
 
1.2%
320 1
 
1.2%
152.1000061 1
 
1.2%
189.1999969 1
 
1.2%
144.3999939 1
 
1.2%
28.79999924 1
 
1.2%
504.2999878 1
 
1.2%
171.6999969 1
 
1.2%
210.3000031 1
 
1.2%
156.6999969 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
25.5 1
1.2%
28.79999924 1
1.2%
35.5 1
1.2%
48.40000153 1
1.2%
58.79999924 1
1.2%
60.09999847 1
1.2%
61.20000076 1
1.2%
72.90000153 1
1.2%
74.09999847 1
1.2%
79.19999695 1
1.2%
ValueCountFrequency (%)
504.2999878 1
1.2%
495.5 1
1.2%
412.2000122 1
1.2%
408 1
1.2%
400.2999878 1
1.2%
337.1000061 1
1.2%
320.5 1
1.2%
320 1
1.2%
303.8999939 1
1.2%
285 1
1.2%

SiteEnergyUse(kBtu)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18807902
Minimum318364.81
Maximum4.4838531 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:01.166353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum318364.81
5-th percentile1437495.5
Q14883656.9
median6915820.2
Q316053282
95-th percentile47428510
Maximum4.4838531 × 108
Range4.4806695 × 108
Interquartile range (IQR)11169625

Descriptive statistics

Standard deviation50036076
Coefficient of variation (CV)2.6603752
Kurtosis67.321488
Mean18807902
Median Absolute Deviation (MAD)3677197.4
Skewness7.8552245
Sum1.5798637 × 109
Variance2.5036089 × 1015
MonotonicityNot monotonic
2023-08-01T12:38:01.319039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7226362.5 1
 
1.2%
16402563 1
 
1.2%
23658978 1
 
1.2%
19459304 1
 
1.2%
13795954 1
 
1.2%
1049748.625 1
 
1.2%
15473117 1
 
1.2%
7258758.5 1
 
1.2%
16701055 1
 
1.2%
3706100.25 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
318364.8125 1
1.2%
875943.3125 1
1.2%
1049748.625 1
1.2%
1247362 1
1.2%
1310236.625 1
1.2%
2158629 1
1.2%
2646879 1
1.2%
2726369 1
1.2%
2951331.25 1
1.2%
3234787.75 1
1.2%
ValueCountFrequency (%)
448385312 1
1.2%
80469216 1
1.2%
72587024 1
1.2%
68090728 1
1.2%
47859812 1
1.2%
44984468 1
1.2%
44731160 1
1.2%
42792072 1
1.2%
39605888 1
1.2%
36667044 1
1.2%

SiteEnergyUseWN(kBtu)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19172557
Minimum326223.09
Maximum4.7161386 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:01.469930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum326223.09
5-th percentile1463437.1
Q15054296.4
median7227405.2
Q316254323
95-th percentile47515775
Maximum4.7161386 × 108
Range4.7128763 × 108
Interquartile range (IQR)11200027

Descriptive statistics

Standard deviation52319434
Coefficient of variation (CV)2.728871
Kurtosis69.37106
Mean19172557
Median Absolute Deviation (MAD)3757598
Skewness8.0152694
Sum1.6104948 × 109
Variance2.7373232 × 1015
MonotonicityNot monotonic
2023-08-01T12:38:01.621315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7456910 1
 
1.2%
16959754 1
 
1.2%
23736924 1
 
1.2%
20194606 1
 
1.2%
13909270 1
 
1.2%
1243027.75 1
 
1.2%
15684371 1
 
1.2%
7250730.5 1
 
1.2%
16527711 1
 
1.2%
3787499 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
326223.0938 1
1.2%
875943.3125 1
1.2%
1239429.75 1
1.2%
1243027.75 1
1.2%
1310236.625 1
1.2%
2331573.25 1
1.2%
2682242.75 1
1.2%
3036464 1
1.2%
3150635.5 1
1.2%
3323400.5 1
1.2%
ValueCountFrequency (%)
471613856 1
1.2%
82318072 1
1.2%
73937112 1
1.2%
49539212 1
1.2%
47602720 1
1.2%
47023088 1
1.2%
45547252 1
1.2%
44110016 1
1.2%
40153432 1
1.2%
38105108 1
1.2%

SteamUse(kBtu)
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1288735.7
Minimum0
Maximum21566554
Zeros63
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:01.752543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3184389.88
95-th percentile5898626.8
Maximum21566554
Range21566554
Interquartile range (IQR)184389.88

Descriptive statistics

Standard deviation3188344.7
Coefficient of variation (CV)2.4740097
Kurtosis20.451158
Mean1288735.7
Median Absolute Deviation (MAD)0
Skewness3.9908472
Sum1.082538 × 108
Variance1.0165542 × 1013
MonotonicityNot monotonic
2023-08-01T12:38:01.881623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 63
75.0%
2003882 1
 
1.2%
5137220 1
 
1.2%
1202380 1
 
1.2%
9763690 1
 
1.2%
4120130 1
 
1.2%
6093150 1
 
1.2%
5327802.5 1
 
1.2%
11598686 1
 
1.2%
3430862 1
 
1.2%
Other values (12) 12
 
14.3%
ValueCountFrequency (%)
0 63
75.0%
737559.5 1
 
1.2%
1202380 1
 
1.2%
1656352.5 1
 
1.2%
1776201.875 1
 
1.2%
2003882 1
 
1.2%
2214446.25 1
 
1.2%
2276286.5 1
 
1.2%
3430862 1
 
1.2%
4120130 1
 
1.2%
ValueCountFrequency (%)
21566554 1
1.2%
11598686 1
1.2%
9763690 1
1.2%
6093150 1
1.2%
5999360.5 1
1.2%
5327802.5 1
1.2%
5237165.5 1
1.2%
5137220 1
1.2%
4870847.5 1
1.2%
4592347.5 1
1.2%

Electricity(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2838236.6
Minimum82434
Maximum44102076
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:02.024984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum82434
5-th percentile216614.75
Q1681805.31
median1239209.2
Q32879216.6
95-th percentile10194183
Maximum44102076
Range44019642
Interquartile range (IQR)2197411.3

Descriptive statistics

Standard deviation5456203.1
Coefficient of variation (CV)1.9223919
Kurtosis39.979062
Mean2838236.6
Median Absolute Deviation (MAD)743511.91
Skewness5.6695559
Sum2.3841187 × 108
Variance2.9770152 × 1013
MonotonicityNot monotonic
2023-08-01T12:38:02.176431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1156514.25 1
 
1.2%
1664565.75 1
 
1.2%
4226010 1
 
1.2%
3218245 1
 
1.2%
3772301 1
 
1.2%
106038.5 1
 
1.2%
2973421.25 1
 
1.2%
779819 1
 
1.2%
2897562.75 1
 
1.2%
824351.625 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
82434 1
1.2%
106038.5 1
1.2%
161743.0938 1
1.2%
167733.5 1
1.2%
209536.5938 1
1.2%
256724.2969 1
1.2%
379058.9063 1
1.2%
379121.0938 1
1.2%
384008.3125 1
1.2%
392082.5938 1
1.2%
ValueCountFrequency (%)
44102076 1
1.2%
14515435 1
1.2%
13348631 1
1.2%
13109951 1
1.2%
10327434 1
1.2%
9439097 1
1.2%
8088294.5 1
1.2%
7138872.5 1
1.2%
6220773 1
1.2%
5351014.5 1
1.2%

Electricity(kBtu)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9684063.1
Minimum281265
Maximum1.5047628 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:02.327788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum281265
5-th percentile739089.6
Q12326319.5
median4228182
Q39823886.8
95-th percentile34782554
Maximum1.5047628 × 108
Range1.5019502 × 108
Interquartile range (IQR)7497567.2

Descriptive statistics

Standard deviation18616565
Coefficient of variation (CV)1.9223919
Kurtosis39.979062
Mean9684063.1
Median Absolute Deviation (MAD)2536863
Skewness5.6695559
Sum8.134613 × 108
Variance3.4657649 × 1014
MonotonicityNot monotonic
2023-08-01T12:38:02.487199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3946027 1
 
1.2%
5679498 1
 
1.2%
14419146 1
 
1.2%
10980652 1
 
1.2%
12871091 1
 
1.2%
361803 1
 
1.2%
10145313 1
 
1.2%
2660742 1
 
1.2%
9886484 1
 
1.2%
2812688 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
281265 1
1.2%
361803 1
1.2%
551867 1
1.2%
572307 1
1.2%
714939 1
1.2%
875943 1
1.2%
1293349 1
1.2%
1293561 1
1.2%
1310236 1
1.2%
1337786 1
1.2%
ValueCountFrequency (%)
150476283 1
1.2%
49526664 1
1.2%
45545529 1
1.2%
44731153 1
1.2%
35237205 1
1.2%
32206199 1
1.2%
27597261 1
1.2%
24357833 1
1.2%
21225277 1
1.2%
18257661 1
1.2%

NaturalGas(therms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77283.406
Minimum0
Maximum2979090
Zeros14
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:02.650654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16805.5891
median22328.615
Q345933.915
95-th percentile229202.38
Maximum2979090
Range2979090
Interquartile range (IQR)39128.326

Descriptive statistics

Standard deviation327121.58
Coefficient of variation (CV)4.2327531
Kurtosis77.098138
Mean77283.406
Median Absolute Deviation (MAD)17867.989
Skewness8.6252063
Sum6491806.1
Variance1.0700853 × 1011
MonotonicityNot monotonic
2023-08-01T12:38:02.802058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
16.7%
12764.5293 1
 
1.2%
6879.452148 1
 
1.2%
68145.70313 1
 
1.2%
8934.120117 1
 
1.2%
107230.6406 1
 
1.2%
309673 1
 
1.2%
13969.80957 1
 
1.2%
29136.03906 1
 
1.2%
26829.20117 1
 
1.2%
Other values (61) 61
72.6%
ValueCountFrequency (%)
0 14
16.7%
371 1
 
1.2%
3851.890137 1
 
1.2%
5273.430176 1
 
1.2%
5315.200195 1
 
1.2%
5324.230469 1
 
1.2%
5512.879883 1
 
1.2%
6584 1
 
1.2%
6879.452148 1
 
1.2%
6886.799805 1
 
1.2%
ValueCountFrequency (%)
2979090 1
1.2%
328535.125 1
1.2%
309673 1
1.2%
296021.5 1
1.2%
233249.9219 1
1.2%
206266.3125 1
1.2%
123314.75 1
1.2%
107230.6406 1
1.2%
103937.3672 1
1.2%
90697.79688 1
1.2%

NaturalGas(kBtu)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7728340.6
Minimum0
Maximum2.97909 × 108
Zeros14
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:02.961457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1680558.75
median2232861.5
Q34593391.5
95-th percentile22920238
Maximum2.97909 × 108
Range2.97909 × 108
Interquartile range (IQR)3912832.8

Descriptive statistics

Standard deviation32712158
Coefficient of variation (CV)4.2327531
Kurtosis77.098138
Mean7728340.6
Median Absolute Deviation (MAD)1786799
Skewness8.6252063
Sum6.4918061 × 108
Variance1.0700853 × 1015
MonotonicityNot monotonic
2023-08-01T12:38:03.114898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
16.7%
1276453 1
 
1.2%
687945 1
 
1.2%
6814570 1
 
1.2%
893412 1
 
1.2%
10723064 1
 
1.2%
30967300 1
 
1.2%
1396981 1
 
1.2%
2913604 1
 
1.2%
2682920 1
 
1.2%
Other values (61) 61
72.6%
ValueCountFrequency (%)
0 14
16.7%
37100 1
 
1.2%
385189 1
 
1.2%
527343 1
 
1.2%
531520 1
 
1.2%
532423 1
 
1.2%
551288 1
 
1.2%
658400 1
 
1.2%
687945 1
 
1.2%
688680 1
 
1.2%
ValueCountFrequency (%)
297909000 1
1.2%
32853512 1
1.2%
30967300 1
1.2%
29602150 1
1.2%
23324992 1
1.2%
20626631 1
1.2%
12331475 1
1.2%
10723064 1
1.2%
10393737 1
1.2%
9069780 1
1.2%

DefaultData
Boolean

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size756.0 B
False
84 
ValueCountFrequency (%)
False 84
100.0%
2023-08-01T12:38:03.264354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Comments
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing84
Missing (%)100.0%
Memory size1.3 KiB

ComplianceStatus
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Compliant
84 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters756
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompliant
2nd rowCompliant
3rd rowCompliant
4th rowCompliant
5th rowCompliant

Common Values

ValueCountFrequency (%)
Compliant 84
100.0%

Length

2023-08-01T12:38:03.365261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-01T12:38:03.488450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compliant 84
100.0%

Most occurring characters

ValueCountFrequency (%)
C 84
11.1%
o 84
11.1%
m 84
11.1%
p 84
11.1%
l 84
11.1%
i 84
11.1%
a 84
11.1%
n 84
11.1%
t 84
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 672
88.9%
Uppercase Letter 84
 
11.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 84
12.5%
m 84
12.5%
p 84
12.5%
l 84
12.5%
i 84
12.5%
a 84
12.5%
n 84
12.5%
t 84
12.5%
Uppercase Letter
ValueCountFrequency (%)
C 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 756
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 84
11.1%
o 84
11.1%
m 84
11.1%
p 84
11.1%
l 84
11.1%
i 84
11.1%
a 84
11.1%
n 84
11.1%
t 84
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 84
11.1%
o 84
11.1%
m 84
11.1%
p 84
11.1%
l 84
11.1%
i 84
11.1%
a 84
11.1%
n 84
11.1%
t 84
11.1%

Outlier
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing84
Missing (%)100.0%
Memory size1.3 KiB

TotalGHGEmissions
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean577.43845
Minimum3.93
Maximum16870.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:03.609580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.93
5-th percentile33.392
Q1128.1525
median235.205
Q3484.16
95-th percentile1722.961
Maximum16870.98
Range16867.05
Interquartile range (IQR)356.0075

Descriptive statistics

Standard deviation1860.1592
Coefficient of variation (CV)3.2213983
Kurtosis73.158082
Mean577.43845
Median Absolute Deviation (MAD)152.09
Skewness8.3159686
Sum48504.83
Variance3460192.3
MonotonicityNot monotonic
2023-08-01T12:38:03.761203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
249.98 1
 
1.2%
609.1 1
 
1.2%
607.41 1
 
1.2%
526.85 1
 
1.2%
138.85 1
 
1.2%
39.06 1
 
1.2%
481.97 1
 
1.2%
262.75 1
 
1.2%
430.84 1
 
1.2%
67.06 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
3.93 1
1.2%
6.11 1
1.2%
9.13 1
1.2%
29.04 1
1.2%
33.26 1
1.2%
34.14 1
1.2%
35.36 1
1.2%
39.06 1
1.2%
44.32 1
1.2%
45.43 1
1.2%
ValueCountFrequency (%)
16870.98 1
1.2%
2451.58 1
1.2%
2089.28 1
1.2%
1990.5 1
1.2%
1727.11 1
1.2%
1699.45 1
1.2%
1265.29 1
1.2%
804.2 1
1.2%
802.89 1
1.2%
740.97 1
1.2%

GHGEmissionsIntensity
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6097619
Minimum0.04
Maximum34.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-08-01T12:38:03.920530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.406
Q10.9575
median1.865
Q32.865
95-th percentile6.3745
Maximum34.09
Range34.05
Interquartile range (IQR)1.9075

Descriptive statistics

Standard deviation3.9513734
Coefficient of variation (CV)1.5140743
Kurtosis49.455428
Mean2.6097619
Median Absolute Deviation (MAD)0.985
Skewness6.3937491
Sum219.22
Variance15.613352
MonotonicityNot monotonic
2023-08-01T12:38:04.073810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.44 4
 
4.8%
0.6 2
 
2.4%
1.4 2
 
2.4%
0.64 2
 
2.4%
1.6 2
 
2.4%
0.8 1
 
1.2%
1.86 1
 
1.2%
0.67 1
 
1.2%
4.83 1
 
1.2%
3.43 1
 
1.2%
Other values (67) 67
79.8%
ValueCountFrequency (%)
0.04 1
 
1.2%
0.1 1
 
1.2%
0.14 1
 
1.2%
0.34 1
 
1.2%
0.4 1
 
1.2%
0.44 4
4.8%
0.58 1
 
1.2%
0.6 2
2.4%
0.64 2
2.4%
0.65 1
 
1.2%
ValueCountFrequency (%)
34.09 1
1.2%
11.12 1
1.2%
7.94 1
1.2%
6.98 1
1.2%
6.52 1
1.2%
5.55 1
1.2%
5.05 1
1.2%
4.83 1
1.2%
4.75 1
1.2%
4.67 1
1.2%

Interactions

2023-08-01T12:37:45.250285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:58.947708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.731178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:06.279688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:16.940641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.680063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:24.071046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.872593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.639621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:35.325759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:39.159216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.804996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.868014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.489280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.386969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.574170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.945864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.639813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.724911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:12.205682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.687597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:19.301210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.782715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:26.910486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.366797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:34.240209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.886006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.542395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:45.379585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:59.066900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.847284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:06.649926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:17.063164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.795716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:24.191601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.993534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.769896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:35.440237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:39.278248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.938176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.980749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.609045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.502841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.686525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:01.085356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.755785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.843143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:12.322331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.803447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:19.421358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.914231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.028553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.508835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:34.363924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.017067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.664217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:45.495384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:59.183344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.958873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:07.157754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:17.179000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.895772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:24.308358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:28.125920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.891385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:35.568363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:39.390709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:43.070040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:47.107437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.740328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.605535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.787420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:01.214778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.872046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.958711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:12.436900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.922158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:19.532708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.019540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.158642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.628292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:34.478519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.131169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.790299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.025356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:59.786743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:03.480443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:07.869906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:17.681112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.419337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:24.820395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:28.753825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.424063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.084588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:39.925698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:43.596334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:47.643506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:51.062582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.801337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.172694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:01.767581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.412910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:09.478710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:12.948550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:16.447078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.049979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.550187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.673883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.154337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:34.997288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.656310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.314872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.145049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:59.896818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:03.586557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:08.236709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:17.799105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.525687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:24.938122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:28.884316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.544143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.203362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.044187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:43.719069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:47.762309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:51.579187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.904426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.286527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:01.888496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.523349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:09.589403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.053101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:16.565692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.159963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.660598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.781935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.271912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.118999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.774097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.441069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.251225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:35:59.997141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:03.686687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:08.592676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:17.899124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.609934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.023270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:28.994915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.645773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.303225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.144437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:43.823580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:47.865446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:51.689304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.023609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.407532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:01.998904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.623444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:09.688037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.166633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:16.681273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.259578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.754143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.884578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.371885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.219016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.889592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.545061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.355472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.113038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:03.802425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:08.957914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.008149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.708601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.138485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.112214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.759746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.403512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.260242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:43.937025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:47.972941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:51.797784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.154822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.510436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.114644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.742325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:09.786070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.266638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:16.796905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.352239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.850628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:27.992025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.487623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.336726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:38.989626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.645230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.471152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.228702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:03.918238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:09.323020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.115027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.823756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.238646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.225815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.875569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.503631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.360347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.052836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.083030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:51.928944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.257951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.607511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.230313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.854736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:09.898384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.366890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:16.901599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.468819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:23.967407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.091925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.603216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.437044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.105427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.776519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.599247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.360202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.036929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:09.707855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.246586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:21.941328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.367619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.354528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:32.994563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.636225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.491608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.199429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.214712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.044771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.370938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.723315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.353667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:05.971543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.014169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.482565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.033802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.589339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.077994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.222233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.732072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.568446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.238940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:42.892013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.702434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.461772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.146049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:10.065445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.358233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.038381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.469885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.455962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.110398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.735147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.612750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.315095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.314489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.154972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.472022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.837406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.453673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.086308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.123072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.590034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.150526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.695509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.193470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.307653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.849618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.683947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.348761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.014123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.813465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.576305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.260994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:10.441907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.460276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.138418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.576037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.593809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.231432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.840063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.723861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.437940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.427232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.286139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.587640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:58.952898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.600928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.202038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.229862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.699070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.267629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.795510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.293841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.423239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:31.972881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.798330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.464519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.139698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:46.954051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.701684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.376864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:10.823415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.592575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.254225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.708851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.724252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.362068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:36.955806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.845975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.569388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.563641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.398637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.703453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.071573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.723111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.325234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.351651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.833232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.398913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:20.926646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.424952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.539075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.108953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:35.932416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.590476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.266036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.068316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.823447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.497718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:11.192543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.700574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.370091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.807262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.842400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.477959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.078120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:40.971664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.715293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.673227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.529817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.805315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.175869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.842305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.443931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.452702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:13.933640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.517130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.050047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.532912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.651073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.225058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.038039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.715430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.376023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.186825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:00.935186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.611327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:11.443810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.827091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.477274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:25.923703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:29.973732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.593855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.192286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.090913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.838467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.786804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.645669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:55.924975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.292767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:02.953381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.556503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.573201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.052146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.653863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.166793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.649549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.765714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.347466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.172694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.831934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.507163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.286780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.036669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.714645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:11.605783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:18.929475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.590997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.041161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.074240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.690463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.293436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.194631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:44.969686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:48.888711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.755611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.024959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.392741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.059827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:06.656420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.689318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.151982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.765394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.268783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.767322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.869833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.449856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.269339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:39.947709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.607273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.402423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.146048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.810203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:11.881377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.233813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.708584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.141410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.194837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.809541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.388947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.305228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.098562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.005213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.854875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.124900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.492741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.153335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.262905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.786803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.268516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.869995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.367554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.867476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:28.969982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.590078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.383597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.048852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.731427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.518332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.257676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:04.937896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:12.254321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.347780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.808675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.265253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.306551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:33.922142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.506993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.421964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.223269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.120989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:52.971444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.225012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.600038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.264890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.368717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:10.905704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.368849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:17.984753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.480808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:24.979275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.088088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.710041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.499033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.168477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.858613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.633824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.377828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.045353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:12.814498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.459231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:22.925139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.373108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.431931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.047697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.625004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.547366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.372014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.236903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.088953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.340706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.693301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.380610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.480648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.000551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.484811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.104603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.596380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.095196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.193299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.851440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.618948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.276227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:43.964970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.748351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.487434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.145402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:13.198899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.568558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.025268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.474879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.524247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.147830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:37.726327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.647420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.487920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.338460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.202800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.440691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.793209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.480611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.580615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.100669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.592417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.217099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.696240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.201920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.293448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:32.966146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.731543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.391994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.077756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.864293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.591432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.250090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:13.588720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.663424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.110606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.575816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.644990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.263649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.140127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.763272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.620105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.456114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.305699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.556887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:59.912493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.580742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.696203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.200778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.689353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.318317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.796398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.308997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.393469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.081851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.838425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.492162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.201497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:47.969302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.697782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.361875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:13.964712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.774565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.209556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.690481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.747389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.363631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.258014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.879106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.797832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.555371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.421144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.657376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.008984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.696178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.796494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.316436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.785606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.418321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:21.896465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.395750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.493410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.215251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:36.954089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.607879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.312742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.080084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.809460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.464986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:14.338600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.879151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.310260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.791670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.843303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.482400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.358042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:41.979208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:45.961770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.669864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.545632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.760362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.124644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.805363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:07.912045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.416765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:14.902105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.523451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.010770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.511467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.593584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.337341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.053979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.724178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.425084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.189517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:01.909446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.577469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:14.696532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:19.979108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.408683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.890746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:30.956817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.594856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.458090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.089504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.082086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.771733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.655538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.872983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.224592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:03.904442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.012146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.516949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.002820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.616440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.112698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.601698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.693893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.457982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.175006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.831843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.525231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.297909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.025144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.677370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:15.062384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.079284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.510480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:26.991218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.057857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.706148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.573752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.206108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.213421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:49.889739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.770180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:56.973910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.324901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.019020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.120570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.620148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.101851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.717090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.212946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.719675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.794167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.576120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.285700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:40.941914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.647247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.422967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.140956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.803024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:15.441873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.210739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.624217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.122089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.184839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.838229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.695949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.321929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.355985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.007624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:53.902448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.087703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.443461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.136570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.250098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.732756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.220753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.846514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.332579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.838180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:29.910013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.707427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.413739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.064053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.765790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.547995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.256870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:05.909745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:15.815084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.326422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.739698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.240411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.300621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:34.962819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.807453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.448294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.489179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.123359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.022747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.207400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.568377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.268698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.369343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.853292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.335019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:18.951328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.449739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:25.955911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.025789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.850649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.516546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.177706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:44.894397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.707748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.376087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:06.035082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:16.184859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.441344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.839955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.627223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.413818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:35.079117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:38.922108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.564078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.613540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.239280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.138390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.326991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.704557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.389696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.491003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:11.968891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.450670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:19.067173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.551829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:26.068129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.148847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:33.977381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.641492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.299888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:45.010245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:48.838853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:02.509118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:06.153672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:16.566869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:20.557845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:23.967904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:27.751049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:31.524343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:35.209358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:39.039226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:42.696609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:46.733706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:50.356068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:54.269825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:36:57.458777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:00.830151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:04.521652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:08.603776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:12.088674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:15.571898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:19.186812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:22.682423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:26.182194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:30.265704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:34.110365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:37.770055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:41.409262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-01T12:37:45.130296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-01T12:38:04.255277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
OSEBuildingIDZipCodeTaxParcelIdentificationNumberCouncilDistrictCodeLatitudeLongitudeYearBuiltNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)LargestPropertyUseTypeGFASecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeGFAENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kWh)Electricity(kBtu)NaturalGas(therms)NaturalGas(kBtu)TotalGHGEmissionsGHGEmissionsIntensityBuildingTypePrimaryPropertyTypeNeighborhoodNumberofBuildingsLargestPropertyUseTypeSecondLargestPropertyUseTypeThirdLargestPropertyUseType
OSEBuildingID1.0000.4820.695-0.3200.409-0.1990.166-0.494-0.112-0.208-0.109-0.105-0.1820.3110.341-0.215-0.229-0.127-0.143-0.209-0.216-0.252-0.097-0.097-0.194-0.194-0.274-0.3130.3700.3360.4130.3500.3210.0000.000
ZipCode0.4821.0000.290-0.3980.247-0.0560.166-0.413-0.025-0.127-0.0310.0340.0900.1470.200-0.153-0.165-0.158-0.163-0.114-0.120-0.227-0.062-0.062-0.099-0.099-0.152-0.1720.0870.2390.4950.0000.1430.2630.475
TaxParcelIdentificationNumber0.6950.2901.000-0.0120.233-0.3730.217-0.210-0.0520.052-0.093-0.041-0.0810.0960.277-0.038-0.0520.0080.002-0.052-0.059-0.1170.0180.018-0.048-0.048-0.086-0.1280.1570.3850.3510.2220.4711.0001.000
CouncilDistrictCode-0.320-0.398-0.0121.000-0.099-0.5110.0400.3550.1090.3070.0640.1070.094-0.4800.129-0.075-0.081-0.036-0.0310.0470.0350.3240.0570.057-0.128-0.1280.045-0.1160.2840.4250.8540.3410.3380.1570.479
Latitude0.4090.2470.233-0.0991.000-0.2630.169-0.385-0.187-0.063-0.178-0.174-0.060-0.1880.203-0.288-0.286-0.334-0.338-0.355-0.348-0.243-0.366-0.366-0.068-0.068-0.303-0.2470.6930.4640.6970.5160.3460.4550.616
Longitude-0.199-0.056-0.373-0.511-0.2631.000-0.0990.101-0.027-0.091-0.008-0.0350.0660.283-0.191-0.012-0.002-0.028-0.025-0.033-0.023-0.098-0.050-0.0500.0600.060-0.0330.0310.4470.4260.5600.4340.3690.4240.000
YearBuilt0.1660.1660.2170.0400.169-0.0991.0000.1160.2690.5450.1500.1910.3840.2190.1360.0740.0440.1650.1520.1950.171-0.0810.2990.2990.0970.0970.067-0.1440.0590.2220.1740.2220.2230.0050.095
NumberofFloors-0.494-0.413-0.2100.355-0.3850.1010.1161.0000.4190.4620.3770.3870.248-0.266-0.1840.2670.2690.2420.2450.4370.4370.2610.3780.3780.3740.3740.4480.2480.0000.0000.0000.0000.0000.0000.116
PropertyGFATotal-0.112-0.025-0.0520.109-0.187-0.0270.2690.4191.0000.3550.9540.8940.7080.0640.0060.003-0.0140.0670.0480.7600.7670.2050.7270.7270.3520.3520.652-0.0230.7200.3840.1520.4600.3360.1830.000
PropertyGFAParking-0.208-0.1270.0520.307-0.063-0.0910.5450.4620.3551.0000.1500.2380.349-0.431-0.0070.0710.0650.1450.1440.2070.2000.0850.2690.2690.2000.2000.178-0.1160.0000.0000.0000.0000.0000.0000.000
PropertyGFABuilding(s)-0.109-0.031-0.0930.064-0.178-0.0080.1500.3770.9540.1501.0000.8850.6420.144-0.057-0.013-0.0280.0330.0120.7630.7720.2510.7020.7020.3250.3250.6650.0260.6990.2500.0000.2510.1620.0000.134
LargestPropertyUseTypeGFA-0.1050.034-0.0410.107-0.174-0.0350.1910.3870.8940.2380.8851.0000.6300.020-0.071-0.028-0.047-0.013-0.0330.7430.7490.1970.6710.6710.4020.4020.6640.0540.3380.3550.0670.4670.3400.0000.000
SecondLargestPropertyUseTypeGFA-0.1820.090-0.0810.094-0.0600.0660.3840.2480.7080.3490.6420.6301.0000.142-0.0460.028-0.0010.0970.0750.6170.6240.1200.6150.6150.2680.2680.485-0.0270.6600.4940.1060.3530.0000.3650.437
ThirdLargestPropertyUseTypeGFA0.3110.1470.096-0.480-0.1880.2830.219-0.2660.064-0.4310.1440.0200.1421.0000.2910.0790.0670.1640.1690.3100.300-0.2180.2620.2620.0840.0840.2370.1460.6700.5950.5850.6700.4200.7950.469
ENERGYSTARScore0.3410.2000.2770.1290.203-0.1910.136-0.1840.006-0.007-0.057-0.071-0.0460.2911.000-0.691-0.691-0.683-0.689-0.342-0.335-0.333-0.335-0.335-0.205-0.205-0.396-0.5470.5800.3110.0740.3460.2310.3100.511
SiteEUI(kBtu/sf)-0.215-0.153-0.038-0.075-0.288-0.0120.0740.2670.0030.071-0.013-0.0280.0280.079-0.6911.0000.9960.9140.9140.5350.5220.2570.4580.4580.3800.3800.5800.7990.3390.4240.1280.1460.3750.0000.000
SiteEUIWN(kBtu/sf)-0.229-0.165-0.052-0.081-0.286-0.0020.0440.269-0.0140.065-0.028-0.047-0.0010.067-0.6910.9961.0000.9050.9080.5160.5080.2480.4360.4360.3890.3890.5740.8090.6920.4310.1970.0720.4350.2430.000
SourceEUI(kBtu/sf)-0.127-0.1580.008-0.036-0.334-0.0280.1650.2420.0670.1450.033-0.0130.0970.164-0.6830.9140.9051.0000.9970.5410.5280.2390.6040.6040.1980.1980.4630.5580.4080.2980.1060.1250.3510.3300.496
SourceEUIWN(kBtu/sf)-0.143-0.1630.002-0.031-0.338-0.0250.1520.2450.0480.1440.012-0.0330.0750.169-0.6890.9140.9080.9971.0000.5220.5120.2400.5860.5860.1910.1910.4510.5620.4650.2590.0000.0000.2390.3880.394
SiteEnergyUse(kBtu)-0.209-0.114-0.0520.047-0.355-0.0330.1950.4370.7600.2070.7630.7430.6170.310-0.3420.5350.5160.5410.5221.0000.9970.3750.9100.9100.4750.4750.9070.4720.7010.3250.3410.2740.0000.4780.545
SiteEnergyUseWN(kBtu)-0.216-0.120-0.0590.035-0.348-0.0230.1710.4370.7670.2000.7720.7490.6240.300-0.3350.5220.5080.5280.5120.9971.0000.3770.9020.9020.4810.4810.9130.4740.7010.3250.3410.2740.0000.4780.545
SteamUse(kBtu)-0.252-0.227-0.1170.324-0.243-0.098-0.0810.2610.2050.0850.2510.1970.120-0.218-0.3330.2570.2480.2390.2400.3750.3771.0000.2920.292-0.275-0.2750.4520.3620.1590.0000.0000.0000.0000.0000.000
Electricity(kWh)-0.097-0.0620.0180.057-0.366-0.0500.2990.3780.7270.2690.7020.6710.6150.262-0.3350.4580.4360.6040.5860.9100.9020.2921.0001.0000.2620.2620.6970.2200.6840.3660.2410.3520.2520.2430.263
Electricity(kBtu)-0.097-0.0620.0180.057-0.366-0.0500.2990.3780.7270.2690.7020.6710.6150.262-0.3350.4580.4360.6040.5860.9100.9020.2921.0001.0000.2620.2620.6970.2200.6840.3660.2410.3520.2520.2430.263
NaturalGas(therms)-0.194-0.099-0.048-0.128-0.0680.0600.0970.3740.3520.2000.3250.4020.2680.084-0.2050.3800.3890.1980.1910.4750.481-0.2750.2620.2621.0001.0000.6140.5280.7000.5970.6510.4520.4390.4780.806
NaturalGas(kBtu)-0.194-0.099-0.048-0.128-0.0680.0600.0970.3740.3520.2000.3250.4020.2680.084-0.2050.3800.3890.1980.1910.4750.481-0.2750.2620.2621.0001.0000.6140.5280.7000.5970.6510.4520.4390.4780.806
TotalGHGEmissions-0.274-0.152-0.0860.045-0.303-0.0330.0670.4480.6520.1780.6650.6640.4850.237-0.3960.5800.5740.4630.4510.9070.9130.4520.6970.6970.6140.6141.0000.6900.7020.3520.4470.2230.0000.4860.545
GHGEmissionsIntensity-0.313-0.172-0.128-0.116-0.2470.031-0.1440.248-0.023-0.1160.0260.054-0.0270.146-0.5470.7990.8090.5580.5620.4720.4740.3620.2200.2200.5280.5280.6901.0000.6830.4630.4120.3190.3790.3670.356
BuildingType0.3700.0870.1570.2840.6930.4470.0590.0000.7200.0000.6990.3380.6600.6700.5800.3390.6920.4080.4650.7010.7010.1590.6840.6840.7000.7000.7020.6831.0000.4780.4140.0000.4020.5800.628
PrimaryPropertyType0.3360.2390.3850.4250.4640.4260.2220.0000.3840.0000.2500.3550.4940.5950.3110.4240.4310.2980.2590.3250.3250.0000.3660.3660.5970.5970.3520.4630.4781.0000.3910.7230.8410.4720.366
Neighborhood0.4130.4950.3510.8540.6970.5600.1740.0000.1520.0000.0000.0670.1060.5850.0740.1280.1970.1060.0000.3410.3410.0000.2410.2410.6510.6510.4470.4120.4140.3911.0000.3910.3000.0000.401
NumberofBuildings0.3500.0000.2220.3410.5160.4340.2220.0000.4600.0000.2510.4670.3530.6700.3460.1460.0720.1250.0000.2740.2740.0000.3520.3520.4520.4520.2230.3190.0000.7230.3911.0000.6100.7090.469
LargestPropertyUseType0.3210.1430.4710.3380.3460.3690.2230.0000.3360.0000.1620.3400.0000.4200.2310.3750.4350.3510.2390.0000.0000.0000.2520.2520.4390.4390.0000.3790.4020.8410.3000.6101.0000.3940.000
SecondLargestPropertyUseType0.0000.2631.0000.1570.4550.4240.0050.0000.1830.0000.0000.0000.3650.7950.3100.0000.2430.3300.3880.4780.4780.0000.2430.2430.4780.4780.4860.3670.5800.4720.0000.7090.3941.0000.516
ThirdLargestPropertyUseType0.0000.4751.0000.4790.6160.0000.0950.1160.0000.0000.1340.0000.4370.4690.5110.0000.0000.4960.3940.5450.5450.0000.2630.2630.8060.8060.5450.3560.6280.3660.4010.4690.0000.5161.000

Missing values

2023-08-01T12:37:49.150927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-01T12:37:49.956057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-01T12:37:50.382716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypePropertyNameAddressCityStateZipCodeTaxParcelIdentificationNumberCouncilDistrictCodeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kWh)Electricity(kBtu)NaturalGas(therms)NaturalGas(kBtu)DefaultDataCommentsComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
012016NonResidentialHotelMayflower park hotel405 Olive waySeattleWA98101.006590000307DOWNTOWN47.61220-122.3379919271.01288434088434HotelHotel88434.0NaNNaNNaNNaNNaN60.081.69999784.300003182.500000189.0000007226362.57456910.02003882.001.156514e+063946027.012764.5293001276453.0FalseNaNCompliantNaN249.982.83
122016NonResidentialHotelParamount Hotel724 Pine streetSeattleWA98101.006590002207DOWNTOWN47.61317-122.3339319961.0111035661506488502Hotel, Parking, RestaurantHotel83880.0Parking15064.0Restaurant4622.0NaN61.094.80000397.900002176.100006179.3999948387933.08664479.00.009.504252e+053242851.051450.8164105145082.0FalseNaNCompliantNaN295.862.86
232016NonResidentialHotel5673-The Westin Seattle1900 5th AvenueSeattleWA98101.006590004757DOWNTOWN47.61393-122.3381019691.041956110196718759392HotelHotel756493.0NaNNaNNaNNaNNaN43.096.00000097.699997241.899994244.10000672587024.073937112.021566554.001.451544e+0749526664.014938.0000001493800.0FalseNaNCompliantNaN2089.282.19
352016NonResidentialHotelHOTEL MAX620 STEWART STSeattleWA98101.006590006407DOWNTOWN47.61412-122.3366419261.01061320061320HotelHotel61320.0NaNNaNNaNNaNNaN56.0110.800003113.300003216.199997224.0000006794584.06946800.52214446.258.115253e+052768924.018112.1308601811213.0FalseNaNCompliantNaN286.434.67
482016NonResidentialHotelWARWICK SEATTLE HOTEL (ID8)401 LENORA STSeattleWA98121.006590009707DOWNTOWN47.61375-122.3404719801.01817558062000113580Hotel, Parking, Swimming PoolHotel123445.0Parking68009.0Swimming Pool0.0NaN75.0114.800003118.699997211.399994215.60000614172606.014656503.00.001.573449e+065368607.088039.9843808803998.0FalseNaNCompliantNaN505.012.88
592016Nonresidential COSOtherWest Precinct810 Virginia StSeattleWA98101.006600005607DOWNTOWN47.61623-122.3365719991.02972883719860090Police StationPolice Station88830.0NaNNaNNaNNaNNaNNaN136.100006141.600006316.299988320.50000012086616.012581712.00.002.160444e+067371434.047151.8164104715182.0FalseNaNCompliantNaN301.813.10
6102016NonResidentialHotelCamlin1619 9th AvenueSeattleWA98101.006600008257DOWNTOWN47.61390-122.3328319261.01183008083008HotelHotel81352.0NaNNaNNaNNaNNaN27.070.80000374.500000146.600006154.6999975758795.06062767.50.008.239199e+052811215.029475.8007802947580.0FalseNaNCompliantNaN176.142.12
7112016NonResidentialOtherParamount Theatre911 Pine StSeattleWA98101.006600009557DOWNTOWN47.61327-122.3313619261.081027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaNNaNNaNNaNNaNNaN61.29999968.800003141.699997152.3000036298131.57067881.52276286.501.065843e+063636655.03851.890137385189.0FalseNaNCompliantNaN221.512.16
8122016NonResidentialHotel311wh-Pioneer Square612 2nd AveSeattleWA98104.009390000807DOWNTOWN47.60294-122.3326319041.0151639840163984HotelHotel163984.0NaNNaNNaNNaNNaN43.083.69999786.599998180.899994187.19999713723820.014194054.00.002.138898e+067297919.064259.0000006425900.0FalseNaNCompliantNaN392.162.39
10152016NonResidentialHotelHotel Monaco Seattle1101 4th AvenueSeattleWA98101.009420001457DOWNTOWN47.60695-122.3341419691.01115316319279133884HotelHotel133884.0NaNNaNNaNNaNNaN30.0119.599998124.300003228.199997233.00000016016644.016646930.05237165.501.813490e+066187627.045918.5000004591850.0FalseNaNCompliantNaN691.264.51
OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypePropertyNameAddressCityStateZipCodeTaxParcelIdentificationNumberCouncilDistrictCodeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kWh)Electricity(kBtu)NaturalGas(therms)NaturalGas(kBtu)DefaultDataCommentsComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
831202016NonResidentialK-12 SchoolHoly Names Academy728 21st Ave EastSeattleWA98112.013388006603EAST47.62644-122.3042919081.061941040194104K-12 SchoolK-12 School194104.0NaNNaNNaNNaNNaN100.032.00000037.09999854.00000060.0999986219841.57204080.00.05.533097e+051887893.043319.4882804331949.0FalseNaNCompliantNaN243.231.25
841212016NonResidentialSmall- and Mid-Sized Office2100 Building2100 24th Ave SSeattleWA98144.014983031163SOUTHEAST47.58408-122.3012520031.03994333575263681Office, ParkingOffice63681.0Parking35752.0NaNNaNNaN63.076.90000278.199997241.500000245.6000064897760.04980737.50.01.435451e+064897760.00.0000000.0FalseNaNCompliantNaN34.140.34
891312016NonResidentialOtherLakeview1208 NE 64th stSeattleWA98115.017975006254NORTHEAST47.67524-122.3153419411.0456521056521OtherOther49000.0NaNNaNNaNNaNNaNNaN129.699997129.899994407.399994408.0000006357632.06367210.00.01.863315e+066357631.00.0000000.0FalseNaNCompliantNaN44.320.78
901322016NonResidentialOtherRoosevelt Square6417 Roosevelt Way NE # 18-28SeattleWA98115.017975007154NORTHEAST47.67559-122.3176919291.022069340206934Other, ParkingOther119146.0Parking0.0NaNNaNNaNNaN35.70000136.79999972.59999872.9000024253577.54384625.50.05.864898e+052001103.022524.7402302252474.0FalseNaNCompliantNaN133.580.65
911362016NonResidentialHotelHoliday Inn Express North Seattle Shoreline14115 Aurora Ave NSeattleWA98133.019260490125NORTHWEST47.73141-122.3458020011.0451390051390Hotel, ParkingHotel51390.0Parking0.0NaNNaNNaN89.070.30000370.400002130.699997130.8999943611538.03616214.00.04.098150e+051398289.022132.4902302213249.0FalseNaNCompliantNaN127.292.48
941392016NonResidentialHotel7066-Seattle-Northgate13300 Stone Avenue NSeattleWA98133.019260494345NORTHWEST47.72551-122.3423020021.0369138069138HotelHotel70609.0NaNNaNNaNNaNNaN78.045.90000247.099998101.599998104.6999973242458.03323400.50.05.283967e+051802889.014395.6816401439568.0FalseNaNCompliantNaN89.021.29
961442016NonResidentialHotelAlexis Hotel1007 First Ave.SeattleWA98104.019746000357DOWNTOWN47.60480-122.3368019041.051909800190980Hotel, ParkingHotel190980.0Parking19051.0NaNNaNNaN98.034.09999834.09999884.00000084.0000006505995.06505995.00.01.290419e+064402910.021030.8398402103084.0FalseNaNCompliantNaN142.390.75
971452016Nonresidential COSOtherBenaroya Hall200 University StreetSeattleWA98111.019747000257DOWNTOWN47.60804-122.3367019981.062841000284100Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly189750.0NaNNaNNaNNaNNaNNaN77.19999778.400002192.500000192.00000014641502.014881950.04311136.02.873101e+069803021.05273.430176527343.0FalseNaNCompliantNaN429.121.51
981472016NonResidentialHospitalSwedish Ballard5300 Tallman Ave NWSeattleWA98107.027677038756BALLARD47.66737-122.3795619544.052853330285333Hospital (General Medical & Surgical), ParkingHospital (General Medical & Surgical)302661.0Parking148865.0NaNNaNNaN14.0225.000000157.300003479.500000251.80000368090728.047602720.00.01.032743e+0735237205.0328535.12500032853512.0FalseNaNCompliantNaN1990.506.98
1001632016Nonresidential COSOtherFire Station 10/FAC/EOC105 5th Ave SSeattleWA98104.052478014652DOWNTOWN47.60112-122.3278520061.0461156061156Office, OtherOther42755.0Office29219.0NaNNaNNaNNaN130.100006132.699997338.299988337.1000069363374.09550716.00.02.036128e+066947270.024161.0410202416104.0FalseNaNCompliantNaN176.752.89